January, 2020

SANGYEOL LEEDepartment of StatisticsSeoul National University

Seoul, 151-747, Korea

PERSONAL:Birthdate: November 16, 1963Birthplace: Seoul, KoreaCitizenship: Republic of Korea

EDUCATION:

University of Maryland, Department of Mathematics Ph.D. Aug 1991Seoul National University, Dept.of Computer Science & Statistics B.S. Feb 1986

THESIS ADVISOR: Ching Zong Wei

TITLE OF THESIS: Testing whether a time series is Gaussian

RESEARCH INTERESTS:

Inference for stochastic processes, Time series analysis, Change Point Analysis.

EXPERIENCE:

· Vice Dean of Admission Office, Seoul National University (2011-2013)

· Professor at Department of Statistics, Seoul National University, (2006-present)

· Associate Professor at Department of Statistics, Seoul National University, (2001-2006)

· Assistant Professor at Department of Statistics, Seoul National University, (1997-2001)

· Assistant Professor at Department of Statistics, Sookmyung Women’s University (1994-1997)

· Instructor at Department of Statistics and Computer Science, Seoul National University(Jun 1993-Feb 1994)

· Visiting Faculty at University of Maryland, Department of Mathematics (Nov 1992-May1993 )

· Visiting Assistant Professor at Academia Sinica, Taipei, Taiwan (Oct 1991- Oct 1992)

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HONORS:

· Korea Gallup Prize (2010)

· ISI (International Statistical Institute) Elected Member (2011)

ACTIVITIES:

· Vice President of Korean Statistical Society (2019-present)

· Research Director of Korean Statistical Society (2009-2011)

· Associate Editor of Sequential Analysis (2003-present)

· Associate Editor of Annals of Institute of Statistical Mathematics (2006-present)

· Associate Editor of Asia and Pacific Financial Markets (2006-present)

· Associate Editor of Journal of Korean Statistical Society (2006-present)

· Associate Editor of Computational Statistics (2014-present)

· Associate Editor of Statistica Sinica (2014-present)

· Associate Editor of Thailand Statistician (2017-present)

· Associate Editor of Statistical Methodology (2006-2013)

· Associate Editor of Journal of Japan Statistical Society

· Associate Editor of Korean Communications in Statistics (2001-2005)

· Member of Korean Statistical Society, ISI, IMS

· Refereed journals: Annals of Institute of Statistical Mathematics, Annals of Statistics,Bernoulli, Communication in Statistics, Computational Statistics and Data Analysis,Econometric Theory, Journal of American Statistical Association, Journal of Businessand Economic Statistics, Journal of Korean Statistical Society, Journal of KoreanMathematical Society, Journal of Multivariate Analysis, Journal of NonparametricStatistics, Journal of Statistical Computation and Simulation, Statistical Method-ology, Journal of Statistical Planning and Inference, Journal of Time Series Analy-sis, Korean Communications in Statistics, Oxford Bulletin of Economics and Statis-tics, Scandinavian Journal of Statistics, Sequential Analysis, Statistical Inference forStochastic Processes, Statistical Papers, Statistics, Statistics and Probability Letters,Stochastic Processes and their Applications, TEST, The IEEE Transactions on SignalProcessing,

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SUPERVISION:· Ph.D Students

[1] Seongryong Na. The nonparametric test for goodness of fit and change point analysisin time series (2001).

[2] Jeongcheol Ha. Test for parameter constancy in time series models (2002).

[3] Siyun Park. Cusum of squares test for scale change in time series models (2002).

[4] Eunhee Kim. Test for independence and causality in time series models (2003).

[5] Youngjin Kim. The diagnostic test in unstable autoregressive models (2005).

[6] Okyoung Na. Test for structural changes in time series models (2006).

[7] Taewook Lee. Inference for hidden markov and mixture models (2007).

[8] Junmo Song. Statistical inferences for discrete and continuous time stochastic processes:robust estimation and change point analysis (2008).

[9] Mooseob Kim. Robust estimation and change point test for tail index (2009).

[10] Jungsik Noh. Quantile regression estimator for GARCH models(2010).

[11] Byungsoo Kim. Robust estimation for the covariance matrix and copula parameter inmultivariate time series (2012).

[12] Jiyeon Lee. Robust estimation for the covariance matrix and copula parameter inmultivariate time series (2013).

[13] Jiwon Kang. Parameter Change Test and Robust estimation in Integer-Valued TimeSeries Models (2013).

[14] Haejun Oh. Goodness of fit and change point test for ACD models and explosiveperiod detection of financial time series (2016).

[15] Minjo Kim. Inferences for heteroscedastic location-scale time series models with ap-plication to VaR and ES Estimation (2016).

[16] Youngmi Lee. Parameter change test for time series of counts (2017).

[17] Jaewon Huh. Monitoring methods for time series & panel data models with applicationto statistical process control (2017).

[18] Hanwool Kim. Statistical process control in count time series models (2018).

[19] Liang-Ching Lin. Robust estimation for the covariance matrix and copula parameterin multivariate time series (2012), co-advising with M. Guo, National Sun Yat SenUniversity.

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[20] Cheng-Siang Wang. Statistical Inference of Bioinformatics, Wireless Communicationand Economic Models (2012), co-advising with M. Guo, National Sun Yat Sen Uni-versity.

· Master Students

[1] Eunhee Kim. Sequential estimation in infinite order moving average model (1996).

[2] Sookyung Lee. Trimmed mean in time series models (1996).

[3] Ducklyun Kim. A study on the test of variance change in time series model (1997).

[4] Young Eun Jung. Review of ARCH model: variation and extension (1999).

[5] Yeongsook Oh. Nonparametric goodness of fit test in AR(1) model (1999).

[6] Ji Hyun Song. Simulation on the best model identification procedure among generalinformation criteria (1999).

[7] Soojeong Kang. Review of locally stationary process (2000).

[8] Woo Tae Park. A review on causality analysis in stochastic processes (2000).

[9] Okyoung Na. The first passage time and nonlinear renewal theory (2001).

[10] Bangwon Ko. On the review of Cox regression with time dependent coefficients (2001).

[11] Yunha Choi. A study on generalized linear model with time series data (2003).

[12] Chanhyung Hahn. Test for change point of tail index (2004).

[13] Youngsook Jeon. Ljung-Box test for unit root AR-ARCH model (2004).

[14] Jiyun Lee. Time series analysis based on GARCH models (2005).

[15] Hyungnam Kim. Simulation study on residual empirical processes for diffusion pro-cesses (2005).

[16]Haean Nam. Change point analysis in AR-ARCH models (2006).

[17] Sunju Lee. Mosum test in time series (2005).

[18] Heewung Chung. Bickel and Rosenblatt test for GARCH Models (2006).

[19] Seungwon Chung. Output convergence in Asian 6 economies with time series approach(2008).

[20] Yonga Won. Correlation analysis of stock indices based on co-integration method(2008).

[21] Jiyu Sun. Analysis of the Korea business cycle by the Markov switching model (2008).

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[22] Junghun Hong. Channel estimation techniques for wireless OFDM systems (2008).

[23] Yangju Seo. KOSPI200 call option data analysis using duan GARCH model (2009).

[24] Namhee Yun. A Review on CreditRisk+ (2010).

[25] Minjo Kim. Review on the application of Markowitz’s Portfolio optimization byutilizing large dimensional random matrix theory (2012).

[26] Haejune Oh. Review of the spectral analysis of large dimensional random matrices(2012).

[27] Han Meng. A goodness of fit test for threshold GARCH models (2013).

[28] Heeseok Lee. A review on bayesian financial time series analysis (2014).

[29] Eunsil Seok. A review of sequential changepoint detection in quality control anddynamical systems (2015).

[30] Yingshi Xu. VaR forecasting for PM10 data using time series models (2016).

[31] Jihoon Jung. Efficient estimation of influenza epidemics based on seasonal ARIMAmodels (2016).

[32] Jeongae Kim. Comparison study of neural network methods for electricity forecasting(2017).

[33] Miteum Moon. Change point test based on support vector machine (2019).

[34] Seongwoo Seok. A review study on asymptotic normality and parameter change testfor zero-inflated general integer-valued GARCH models (2019).

[35] Yongjin Jeong. Nonlinear ARMA-GARCH forecasting for s&p500 index based onrecurrent neural networks (2019).

[36] Kitae Kang. Forecasting power consumption with various methods and comparisonof these methods (2020).

Invited Talks (2011-2019):

· The 3rd International Conference on Econometrics and Statistics (EcoSta 2019), Co-chair,Taichung, Taiwan, June 25-27, 2019

· The 12th International Conference of the Thailand Econometric Society (TES2019),Chiang Mai, Thailand, January 9-11, 2019

· The 11th International Conference of The Thailand Econometric Society 2018 (TES2018),Chiang Mai, Thailand, January 10-12, 2018

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· 1st International Conference on Econometrics and Statistics, Hong Kong, China, June15-17, 2017

· The second ISI Regional Statistics Conference (ISI RSC 2017), Bali, Indonesia, March22-24, 2017

· The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting, Hong Kong,China, June 26-July 1, 2016

·GOF DAYS 2015: 2nd Workshop on Goodness-of-Fit and Change-point problems, Athens,Greece, September 3-8, 2015

· International Conference on Applied Statistics 2015 “Statistics for Global Evolution Vi-sion in 21st Century”, Pattaya, Thailand, July 15-17, 2015

· 21st International Conference on Computational Statistics, Geneva, Swiss, August 19-22,2014

· The 3rd Institute of Mathematical Statistics Asia Pacific Rim Meeting, Taipei, Taiwan,June 29-July 3, 2014

· International Conference on Applied Statistics (ICAS) 2014, Khon Kaen, Thailand, May21-24, 2014

· Bernoulli Society Satellite Meeting to the ISI World Statistics Congress 2013, Tokyo,Japan, September 4-9, 2013

Grants:

· Inference for various time series models and hybrid study with clustering method formultiple change point detection. Korea NRF grant (2018-2020)

· A study on diagnostics, change point detection and risk management for various timeseries models with volatility based on the bootstrap method. Korean NRF grant(2015-2018)

· Inference for time series with time varying volatility and multi-dimensional time seriesmodels. Korean NRF grant (2012-2015)

· Entropy and characteristic function based goodness of fit test for continuous and discretetime series Korean NRF grant (2011-2014)

· Study on multivariate non-Gaussian financial time series models and extreme risk man-agement. Korean NRF grant (2009-2012)

· Inference for advanced financial and econometric time series models. Korean NRF grant(2006-2009)

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· Inference for volatile financial time series & spatial models. Korean NRF grant (2006-2007)

· Inference for spatio-temporal and jump diffusion models. Korean NRF grant (2003-2006)

· Inference for complex time series models. Korean NRF grant (2002-2003)

· Temporal and spatial process analysis & its applications. Korean NRF grant (1999-2001)

· Reliability estimation for time series models. Korean NRF grant (1998-1999)

· On the conditional tolerance probability in time series models. Korean NRF grant (1996-1997)

PUBLICATION

Articles in International Journals

[1] Fakhre-Zakeri, I. and Lee, S. (1992). Sequential estimation of the mean of a linearprocess. Sequential Analysis. 11, 181-197.

[2] Fakhre-Zakeri, I. and Lee, S. (1993). Sequential estimation of the mean vector of amultivariate linear process. Journal of Multivariate Analysis. 47, 196-209.

[3] Lee, S. (1994). Sequential estimation of the parameters of a stationary autoregressivemodel. Sequential Analysis. 13, 301-317.

[4] Lee, S. (1994). Random central limit theorem of a stationary lattice process. Journalof the Korean Statistical Society. 23, 503-512.

[5] Lee, S. (1995). A trimmed mean of an AR(∞) stationary process. Journal of StatisticalPlanning and Inference. 48, 131-140.

[6] Lee, S. (1995). A note on the strong mixing property for a random coefficient autore-gressive process. Journal of the Korean Statistical Society. 24, 243-248.

[7] Lee, S. (1996). Sequential estimation for the autocorrelations of linear processes. Annalsof Statistics. 24, 2233-2249.

[8] Lee, S. (1996). The asymptotic maximin property of chi-squared type tests based onthe empirical process. Statistics and Probability Letters. 29, 285-292.

[9] Lee, S. and Kim, Y. (1996). The asymptotic unbiasedness of S2 in the linear regressionmodel with dependent errors. Journal of the Korean Statistical Society. 25, 235-241.

[10] Lee, S. (1996). Asymptotic relative efficiency of chi-squared type tests based on theempirical process. Journal of the Korean Statistical Society. 25, 337-346.

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[11] Lee, S. (1996). On fixed width confidence bounds for the difference of the means oftwo linear processes. Journal of the Korean Statistical Society. 25, 603-611.

[12] Lee, S. (1997). A note on the residual empirical process in autoregressive models.Statistics and Probability Letters. 32, 405-412.

[13] Lee, S. (1997). Random central limit theorem for the linear process generated by astrong mixing process. Statistics and Probability Letters. 35, 189-196.

[14] Fakhre-Zakeri, I. and Lee, S. (1997). A random functional central limit theoremfor stationary linear processes generated by martingales. Statistics and ProbabilityLetters. 35. 417-422.

[15] Lee, S. (1997). On the conditional tolerance probability in time series models. Journalof the Korean Statistical Society. 26, 407-416.

[16] Lee, S. (1998). The asymptotic behavior of the empirical process based on a lin-ear process under some contiguous alternatives. Journal of Statistical Planning andInference. 67, 1-15.

[17] Lee, S. (1998). On the quantile process based on the autoregressive residuals. Journalof Statistical Planning and Inference. 67, 17-28.

[18] Lee, S. (1998) Coefficient constancy test in a random coefficient autoregressive model.Journal of Statistical Planning and Inference. 74, 93-101.

[19] Park, S., Lee, S. and Hwang, S. Y. (1998). Testing the randomness of the coefficientsin first order autoregressive processes. Journal of the Korean Statistical Society. 27,189-195.

[20] Lee, S. and Park, E. (1998). Confidence intervals for the stress-strength models withexplanatory variables. Journal of the Korean Statistical Society. 27, 435-449.

[21] Lee, S. and Wei, C. Z. (1999). On residual empirical process of stochastic regressionmodels with applications to time series. Annals of Statistics. 27, 237-261.

[22] Lee, S. and Sriram, T. N. (1999). Sequential point estimation of parameters in athreshold AR(1) model. Stochastic Processes and their Applications. 84, 343-355.

[23] Kim, J. and Lee, S. (1999). An iterative algorithm for the Cramer-Von Mises distanceestimator. InterStat. March. no2.

[24] Lee, S. (1999). Rate of convergence of empirical distributions and quantiles of linearprocesses and its application to trimmed mean. Journal of the Korean StatisticalSociety. 28, 435-441.

[25] Lee, S. (1999). Limiting distribution of trimmed least squares estimators in unstableAR(1) models. Journal of the Korean Statistical Society. 28, 151-165.

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[26] Fakhre-Zakeri, I. and Lee, S. (2000). On functional limit theorems for multivariatelinear processes with applications to sequential estimation. Journal of StatisticalPlanning and Inference. 83, 11-23.

[27] Lee, S. and Na, S. (2000). A nonparametric goodness of fit test for strong mixingprocesses. Annales de l’Institut de Statistique de l’Universite de Paris. 44, 3-20.

[28] Kim, S., Cho, S. and Lee, S. (2000). On the cusum test for parameter changes inGARCH (1,1) models. Communications in Statistics. Theory and Methods. 29,445-462.

[29] Lee, S. and Park, S. and Jeon, J. (2000). The cusum of squares test for variancechanges in infinite order autoregressive models. Journal of the Korean StatisticalSociety. 29, 351-361.

[30] Lee, S. and Park, S. (2001). The cusum of squares test for scale changes in infiniteorder moving average processes. Scandinavian Journal of Statistics. 28, 625-644.

[31] Lee, S. and Karagrigoriou, A. (2001). An asymptotic optimal selection of the order ofa linear process. Sankhya Series A. 63, 93-106.

[32] Karagrigoriou, A. and Lee, S. (2001). On the use of model selection in multivariateprocesses with applications in economics. Mathematics and simulation with biological,economical and musicoacoustical applications, 219–224, Math. Comput. Sci. Eng.,WSES Press, Athens.

[33] Ha, J. and Lee, S. (2002). Coefficient constancy test in AR-ARCH models. Statisticsand Probability Letters. 57, 65-77.

[34] Lee, S. and Na, S. (2002). On the Bickel-Rosenblatt test for the first order autore-gressive. Statistics and Probability Letters. 56, 23-35

[35] Kim, E. and Lee, S. (2002). On the causality test in time series models with heavytailed distribution. Communications in Statistics: Simulation and Computation. 32,313-327.

[36] Kim, Y. and Lee, S. (2002). On the Kolmogorov-Smirnov test for testing nonlinearityin time series. Communications in Statistics: Theory and Methods. 31, 299-310.

[37] Lee, S., Ha, J., Na, O. and Na, S. (2003). The cusum test for parameter change intime series models. Scandinavian Journal of Statistics. 30, 781-796.

[38] Lee, S., Na, O. and Na. S. (2003). On the cusum of squares test for variance change innonstationary and nonparametric time series models. Annals of Institute of StatisticalMathematics. 55, 467-485.

[39] Na, M. and Lee, S. (2003). A family of IDMRL tests with unknown turning point.STATISTICS. 37, 457-462.

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[40] Lee, S. (2003). The sequential estimation in stochastic regression model with randomcoefficients. Statistics and Probability Letters. 61, 71-81.

[41] Lee, S. and Lee, S. (2003). Testing heterogeneity for frailty distribution in sharedfrailty model. Communications in Statistics: Theory and Methods. 32, 2245-2253.

[42] Cha, J., Lee, S. and Mi, J. (2004). Bounding the optimal burn-in time for a systemwith two types of failure. Naval Research Logistics. 1091-1101.

[43] Lee, S. and Na, S. (2004). A nonparametric test for the change of the density functionin strong mixing processes. Statistics and Probability Letters. 66, 25-34.

[44] Lee, S. and Lee, T. (2004). Cusum test for parameter change based on the maximumlikelihood estimator. Sequential Analysis. 23, 239-257.

[45] Lee, S., Tokutsu, Y. and Maekawa, K. (2004). The cusum test for parameter changein regression models with ARCH errors. Journal of the Japan Statistical Society. 34,173-188.

[46] Cha, J., Lee, S. and Mi, J. (2004). Comparison of steady system availability withimperfect repair. Applied Stochastic Models in Business and Industry. 20, 27-36.

[47] Lee, S. and Taniguchi, M. (2005). Asymptotic theory for ARCH models: LAN andresidual empirical process. Statistica Sinica. 15, 215-234.

[48] Lee, S. and Na, O. (2005). Test for parameter change in stochastic processes based onconditional least squares estimator. Journal of Multivariate Analysis. 93, 375-393.

[49] Kim, E. and Lee, S. (2005). A test for independence of two stationary infinite orderautoregressive processes. Annals of Institute of Statistical Mathematics. 57, 105-127.

[50] Lee, S. and Na, O. (2005). Test for parameter change based on the estimator minimiz-ing density-based divergence measures. Annals of Institute of Statistical Mathematics.57, 553-573.

[51] Kim, T. and Lee, S. (2005). Kernel density estimator for strong mixing processes.Journal of Statistical Planning and Inference. 133, 273-284.

[52] Shin, C., Lee, S., Lee, T., Shin, K., Yi, H., Kimm, K. and Cho, N. (2005). Prevalenceof insomnia and its relationship to menopausal status in middle-aged Korean women.Psychiatry and Clinical Neurosciences. 59, 395-402.

[53] Lee, S., Nishiyama, Y. and Yosida, N. (2006). Test for parameter change in diffusionprocesses by cusum statistics based on one-step estimators. Annals of Institute ofStatistical Mathematics. 58, 211-222.

[54] Lee, S. and Sriram, T. N. and Wei, X. (2006). Fixed width confidence interval based onminimum Hellinger distance estimator. Journal of Statistical Planning and Inference.136, 4276-4292.

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[55] Na, S., Lee, S. and Park, H. (2006). Sequential empirical process in autoregressivemodels with measurement errors. Journal of Statistical Planning and Inference. 136,4204-4216.

[56] Lee, S. (2006). The Bickel-Rosenblatt test for diffusion processes. Statistics andProbability Letters. 76, 1494-1502.

[57] Lee, S., Park. S., Kawai, K. and Maekawa, K. (2006). Test for parameter change inARIMA models. Communication in Statistics: Theory and Methods. 35, 429-440.

[58] Cha, J., Lee, S. and Jeon, J. (2006). Sequential confidence interval estimation forsystem availability. Quality and Reliability Engineering International. 22, 165-176.

[59] Maekawa, K., Lee, S. Tokutsu, Y. and Park, S. (2006). Cusum test for parameterchanges in GARCH(1,1) models with applications to Tokyo stock data. Far EastJournal of Statistics. 18, 15-23.

[60] Lee, S. and Na, O. (2007). Moving estimates test with time varying bandwidth.Journal of Multivariate Analysis. 98, 1356-1375.

[61] Lee, S., Kim, E. and Kim, Y. (2007). Diagnostic test in unstable autoregressivemodels. STATISTICS. 41, 181-201.

[62] Lee, T. and Lee, S. (2007). Test for parameter change in linear processes based onWhittle’s estimator. Communications in Statistics: Theory and Methods. 36, 2129-2141.

[63] Kim, M. and Lee, S. (2008). Estimation of tail index using minimum density powerdivergence method. Journal of Multivariate Analysis. 99, 2453-2471.

[64] Maekawa, K., Lee., S., Morimoto, T. and Kawai, K. (2008). Jump diffusion model withapplication to the Japanese stock market. Mathematics and Computers in Simulation.78, 223-236.

[65] Lee, S. and Wee, I. (2008). Residual empirical process for diffusion processes. Journalof the Korean Mathematical Society. 45, 683-693.

[66] Lee, T. and Lee, S. (2008). Robust estimation for the order of finite mixture models.Metrika. 68, 365-390.

[67] Lee, S. and Song, J. (2008). Test for parameter change in ARMA models with GARCHinnovations. Statistics and Probability Letters. 78, 1990-1998.

[68] Lu, X., Maekawa, K., and Lee, S. (2008). The CUSUM of squares test for the stabilityof regression models with non-stationary regressors. Economics Letters. 100, 234-237.

[69] Kim, T., Moon, M. and Lee, S. (2008). Large bandwidth asymptotics for NadarayaWatson autoregression estimator. Journal of the Korean Statistical Society. 37, 313-322.

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[70] Lee, T. and Lee, S. (2009). Normal mixture quasi maximum likelihood estimator forGARCH models. Scandinavian Journal of Statistics. 36, 157-170.

[71] Kim, M. and Lee, S. (2009). Test for tail index change in stationary time series withPareto type marginal distribution. BERNOULLI. 15, 325-356.

[72] Kang, J. and Lee, S. (2009). Parameter change test for random coefficient integer-valued autoregressive processes with application to polio data analysis. Journal ofTime Series Analysis. 30, 239-258.

[73] Mattheou, K., Lee, S. and Karagrigoriou, A. (2009). A model selection criterion basedon the BHHJ measure of divergence. Journal of Statistical Planning and Inference.139, 228-235.

[74] Lee, S. and Song, J. (2009). Minimum density power divergence estimator for GARCHmodels. TEST. 18, 316-341.

[75] Song, J. and Lee, S. (2009). Test for parameter change in discretely observed diffusionprocesses. Statistical Inference for Stochastic Processes. 12, 165-183.

[76] Lee, T. and Lee, S. (2009). Consistency of minimizing a penalized density powerdivergence estimator for mixing distribution. Statistical Papers. 50, 67-80.

[77] Lee, S., Lee, Y., and Na, O. (2009). Monitoring distributional changes in autore-gressive models. Invited paper dedicated to Shelley Zacks to honor his 50 years ofmonumental contributions in statistical science. Communications in Statistics: The-ory and Methods. 38, 2969-2982.

[78] Lee, S. and Park, S. (2009).The monitoring test for the stability of regression modelswith nonstationary regressors. Economics Letters. 105, 250-252.

[79] Lee, S. and Ng, C. (2010). Trimmed Portmanteau test for linear processes with infinitevariance. Journal of Multivariate Analysis. 10, 984-998.

[80] Zhang, G. Lee, J. and Lee, S. (2010). Posterior consistency of species sampling.Statistica Sinica. 10, 581-593.

[81] Lee, S. and Matsuda, H. (2010). Jarque-Bera normality test for the driving process ofa discretely observed univariate SDE. Statistical Inference for Stochastic Processes.13, 97-123.

[82] Lee, S. and Lee, T. (2010). Robust estimation for order of hidden Markov models basedon density power divergences. Journal of Statistical Computation and Simulation. 80,1563-5163.

[83] Lee, S. (2010). On the goodness of fit test for discretely observed sample from diffusionprocesses: divergence measure approach. Journal of the Korean Mathematical Society.47, 1137-1146.

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[84] Lee, S., Lee, T., and Park, S. (2010). A note on the Jarque-Bera normality test forGARCH innovations. Journal of the Korean Statistical Society. 39, 93-102.

[85] Na, O., Lee, J. and Lee, S. (2010). Monitoring parameter changes for random coeffi-cient autoregressive models. Journal of the Korean Statistical Society. 39, 269-396.

[86] Lee, S. and Ng, C. (2011). Normality test for multivariate conditional heteroskedasticdynamic regression models. Economics Letters. 111, 75-77.

[87] Kim, M. and Lee, S. (2011). Change point test for tail index for dependent data.Metrika. 74, 297-311.

[88] Lee, S. and Lee, T. (2011). Value at risk forecasting based on Gaussian mixtureARMA-GARCH model. Journal of Statistical Computation and Simulation. 81,1131-1144.

[89] Lee, S. (2011). Change point test for dispersion parameter based on discretely observedsample from SDE models. Bulletin of the Korean Mathematical Society. 48, 839-845.

[90] Kim, B. and Lee, S. (2011). Robust estimation for covariance matrix of multivariatetime series. Journal of Time Series Analysis. 32, 469-481.

[91] Na, O., Lee, Y. and Lee, S. (2011). Monitoring parameter change in time series models.Statistical Methods and Applications. 20, 171-199.

[92] Lee, S., Ilia, V. and A. Karagrigouriou (2011). A maximum entropy type test of fit.Computational Statistics and Data Analysis. 55, 2635-2643.

[93] Lee, S. and Karagrigoriou, A. (2011). A divergence test for autoregressive time seriesmodels. Statistical Methodology. 8, 442-450.

[94] Na, O., Lee, J. and Lee, S. (2011). Constancy test for long memory FARIMA models.Journal of the Korean Statistical Society. 40, 161-172.

[95] Lee, S. and Lee, T. (2012). Inference for Box-Cox transformed threshold GARCHmodels with nuisance parameters. Scandinavian Journal of Statistics. 39, 568-589.

[96] Na, O., Lee, J. and Lee, S. (2012). Change point detection in copula ARMA-GARCHmodels. Journal of Time Series Analysis. 33, 554-569.

[97] Noh, J., Lee, S. and Lee, S. (2012). Quantile regression estimation for discretelyobserved SDE models with compound Poisson jumps. Economics Letters. 117, 734-738.

[98] Lee, S. and Guo, M. (2012). Test for dispersion constancy in SDE models. AppliedStochastic Models in Business and Industry. 28, 342-353.

[99] Kim, M. and Lee, S. (2012). Change point test of tail index for autoregressive pro-cesses. Journal of Korean Statistical Society. 41, 305-312.

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[100] Lee, S. and Park, S. (2013). Maximum entropy test for autoregressive models. InUncertainty Analysis in Econometrics with Applications: Advances in Intelligent Sys-tems and Computing. The series Studies in Computational Intelligence, 200, 119-128.Springer.

[101] Lee, S. and Noh, J. (2013). Quantile regression estimator for GARCH models.Scandinavian Journal of Statistics. 40, 2-20.

[102] Lee, S. (2013). A maximum entropy type test of fit: composite hypothesis case.Computational Statistics and Data Analysis. 57, 59-67.

[103] Lin, L.C., Lee, S. and Guo, M. (2013). Goodness-of-fit test for stochastic volatilitymodels. Journal of Multivariate Analysis. 116, 473-498.

[104] Kim, B. and Lee, S. (2013). Robust estimation for the covariance matrix of mul-tivariate time series based on normal mixtures. Computational Statistics and DataAnalysis. 57, 125-140.

[105] Lee, S. and Song, J. (2013). Minimum density power divergence estimator for diffu-sion processes. Annals of Institute of Statistical Mathematics. 65, 213-236.

[106] Kim, B. and Lee, S. (2013). Robust estimation for copula parameter in SCOMDYmodels. Journal of Time Series Analysis. 34, 302-314.

[107] Na, O., Lee, J. and Lee, S. (2013). Change point detection in SCOMDY models.AStA Advances in Statistical Analysis. 97, 215-238.

[108] Kim, M. and Lee, S. (2013). On the maximum likelihood estimator for irregularlyobserved time series data from COGARCH(1,1) models. REVSTAT. 11, 135-168.

[109] Chen, C. W. S., Chen, S. Y. and Lee, S. (2013). Bayesian unit root test in doublethreshold GARCH models. Computational Economics. 42, 471-490.

[110] Lee, S. (2014). Goodness of fit test for discrete random variables. ComputationalStatistics and Data Analysis. 69, 92-100.

[111] Lee, S. and Lee, J. (2014). Residual based cusum test for parameter change in AR-GARCH models. In Modeling Dependence in Econometrics: Advances in IntelligentSystems and Computing. The series Studies in Computational Intelligence, 251, 101-111. Springer.

[112] Lin, L, Lee, S. and Guo, M. (2014). The Bickel-Rosenblatt test for continuous timestochastic volatility models. TEST. 23, 195-218.

[113] Kang, J. and Lee, S. (2014). Minimum density power divergence estimator for Poissonautoregressive models. Computational Statistics and Data Analysis. 80, 44-56.

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[114] Na, O., Lee, J. and Lee, S. (2014). Monitoring test for stability of copular parameterin time series. Journal of Korean Statistical Society. 43, 483-501

[115] Kang, J. and Lee, S. (2014). Parameter change test for Poisson autoregressive models.Scandinavian Journal of Statistics. 41, 1136-1152.

[116] Kim, B. and Lee, S. (2014). Minimum density power divergence estimator for covari-ance matrix based on skew t distribution. Statistical Methods and Applications. 23,565-575.

[117] Kim, M. and Lee, S. (2014). Change point test for tail index of scale-shifted processes.Statistics and Risk Modeling. 31, 297-333.

[118] Lee, S. and Oh, H. (2015). Entropy test and residual empirical process for autore-gressive conditional duration models. Computational Statistics and Data Analysis.86, 1-12.

[119] Lee, J., Lee, S. and Park, S. (2015). Maximum entropy test for GARCH models.Statistical Methodology. 22, 8-16.

[120] Lee, S. and Guo, M. (2015). Monitoring change point for diffusion parameter basedon discretely observed sample from stochastic differential equation models. AppliedStochastic Models in Business and Industry. 31, 609-625.

[121] Kim, B. and Lee, S. (2015). Copula parameter change test for nonlinear AR modelswith nonlinear GARCH errors. Statistical Methodology. 25, 1-22.

[122] Lee, J. and Lee, S. (2015). Parameter change test for nonlinear time series modelswith GARCH type errors. Journal of Korean Mathematical Society. 52, 503-522.

[123] Hardi, A. S., Kawai, K., Lee, S. and Maekawa, K. (2015). Change point analysisof exchange rates using bootstrapping methods: an application to the IndonesianRupiah. Asia and Pacific Financial Markets. 22, 429-444.

[124] Noh, J. and Lee, S. (2016). Quantile regression for location-scale time series modelswith conditional heteroscedasticity. Scandinavian Journal of Statistics. 43, 700-720.

[125] Kim, M. and Lee, S. (2016). Nonlinear expectile regression with application tovalue-at-risk and expected shortfall estimation. Computational Statistics and DataAnalysis. 94, 1-19.

[126] Kim, M. and Lee, S. (2016). On the tail index inference for heavy-tailed GARCH-typeinnovations. Annals of Institute of Statistical Mathematics. 68, 237-267.

[127] Lin, L.C., Lee, S. and Guo, M. (2016). Goodness-of-fit test for the SVM based onthe noisy observations. Statistica Sinica. 26, 105-1329.

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[128] Chen, C.W.S. and Lee, S. (2016). A local unit root test in mean for financial timeseries. Journal of Statistical Computation and Simulation. 86, 788-806.

[129] Lee, S., Lee, Y. and Chen, C.W.S. (2016). Parameter change test for zero-inflatedgeneralized Poisson autoregressive models. STATISTICS. 50, 540-557.

[130] Lee, S. and Oh, H. (2016). Parameter change test for autoregressive conditionalduration models. Annals of Institute of Statistical Mathematics. 68, 621-637.

[131] Chen, C.W.S. and Lee, S. (2016). Generalized autoregressive Poisson models for timeseries of counts. Computational Statistics and Data Analysis. 99, 51-67.

[132] Noh, J. and Lee, S. (2016). On the identifiability problem in nonlinear time seriesmodels. REVSTAT. 14, 395-413.

[133] Chen, C. W. S., Lee, S. and Chen, S. (2016). Local non-stationarity test in meanfor Markov switching GARCH models: an approximate Bayesian approach. Compu-tational Statistics. 1, 1-24.

[134] Lee, S. (2016). Entropy-based goodness of fit test for a composite hypothesis. Bulletinof Korean Mathematical Society. 53, 351-363.

[135] Chen, C.W.S., Wang, Z., Songsak Sriboonchitta and Lee, S. (2017). Pair tradingbased on quantile forecasting of smooth transition GARCH models. North AmericanJournal of Economics and Finance. 39, 38-55.

[136] Chen, C.W.S. and Lee, S. (2017). Bayesian causality test for integer-valued timeseries models with applications to climate and crime data. Journal of Royal StatisticalSociety C, 66, 797-814.

[137] Lee, S. and Kim, M. (2017). On entropy based goodness of fit test for asymmetricstudent-t and exponential power distributions. Journal of Statistical Computationand Simulation. 87, 187-197.

[138] Kim, M. and Lee, S. (2017). Estimation of the tail exponent of multivariate regularvariation. Annals of Institute Statistical Mathematics. 69, 945-968.

[139] Kim, H. and Lee, S. (2017). On first-order integer-valued autoregressive process withKatz family innovations. Journal of Statistical Computation and Simulation. 87,546-562.

[140] Lee, S., Park, S., and Chen, W.S.C. (2017). On Fishers dispersion test for integer-valued autoregressive Poisson models with applications. Communications in Statistics,Theory and Methods. 46, 9985-9994.

[141] Huh, J., Oh, H. and Lee, S. (2017). Monitoring parameter change for time seriesmodels with conditional heteroscedasticity. Economics Letters. 152, 66-70.

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[142] Huh, J. Kim, H. and Lee, S. (2017). Monitoring parameter change for Poissonautoregressive models. Journal of Statistical Computation and Simulation. 8, 1754-1766.

[143] Lee, S. and Kim, M. (2017). On entropy test for conditionally heteroscedasticlocation-scale time series models. Entropy. 19(8), 388.

[144] Kim, B. and Lee, S. (2017). Robust estimation for zero-inflated poisson autoregressivemodels based on density power divergence. Journal of Statistical Computation andSimulation. 87, 2981-2996.

[145] Xu, Y. and Lee, S. (2017). Qunatile forecasting of PM10 data in Korea based ontime series models. In: Kreinovich V., Sriboonchitta S., Huynh VN. (eds) Robustnessin Econometrics. Studies in Computational Intelligence, vol 692, 587-598, Springer,Cham.

[146] Chen, W.C.S., Khamthong, K., and Lee, S. (2017). Structural breaks of CAPM-type market model with heteroscedasticity and quantile regression. In: KreinovichV., Sriboonchitta S., Huynh VN. (eds) Robustness in Econometrics. Studies in Com-putational Intelligence, vol 692, 111-134. Springer, Cham. Springer.

[147] Lee, Y., Lee, S. and Tjostheim, D. (2018). Asymptotic normality and parameterchange test for bivariate Poisson INGARCH models. TEST, 27, 52-69.

[148] Oh, H., Lee, S. and Chan, N. H. (2018). Mildly explosive autoregression with mixinginnovations. Journal of Korean Statistical Society. 47, 41-53.

[149] Kim, H. and Lee, S. (2018). On the VSI CUSUM Chart for count processes and itsImplementation with R package attrCUSUM. Industrial Engineering & ManagementSystems. 17, 91-101.

[150] Oh, H. and Lee, S. (2018). On score vector- and residual-based CUSUM tests inARMA-GARCH models. Statistical Methods and Applications. 27, 385-406.

[151] Oh, H. and Lee, S. (2018). On change point test for ARMA-GARCH models: Boot-strap approach. Journal of Korean Statistical Society. 47, 139-149.

[152] Lee, S., Park, S. and Kim, B. (2018). On entropy-type goodness of fit test basedon integrated distribution. Journal of Statistical Computation and Simulation. 88,2447-2461.

[153] Kim, H. and Lee, S (2018). Monitoring mean shift in INAR(1)s process using CLSE-CUSUM test procedure. Thailand Statistician. 16, 173-189.

[154] Kim, M. and Lee, S. (2018). Bootstrap entropy test for location-scale time seriesmodels test. Journal of Statistical Computation and Simulation. 13, 2573-2588.

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[155] Oh, H. and Lee, S. (2018). On parameter change test for ARMA models with mar-tingale difference errors. In: Kreinovich V., Sriboonchitta S., Chakpitak N. (eds)Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelli-gence, vol 753. 246-254. Springer, Cham

[156] Lee S., Meintanis S.G., Pretorius C. (2018). Fourier-Type Monitoring Proceduresfor Strict Stationarity. In: Bertail P., Blanke D., Cornillon PA., Matzner-Lober E.(eds) Nonparametric Statistics. ISNPS 2016. Springer Proceedings in Mathematics& Statistics, vol 250. 323-336. Springer, Cham.

[157] Dong, M. C., Chen, C.W.S., Lee, S. and Songsak, S. (2019). How strong is therelationship among gold and USD exchange rates? Analytics based on structuralchange models. Computational Economics. 53, 343-366.

[158] Lee, Y. and Lee, S. (2019). Causality test for time series of counts based on PoissonINGARCH models. Communications in Statistics, Simulation and Computation. 48,1901?1911.

[159] Kim, M. and Lee, S. (2019). Test for tail index constancy of GARCH innovationsbased on conditional volatility. Annals of Institute of Statistical Mathematics. 71,947-981.

[160] Chen, C.W.S., Khamthong, K. and Lee, S. (2019). Markov switching integer-valuedGARCH models for modeling dengue haemorrhagic fever counts. Journal of RoyalStatistical Society. Series C (Applied Statistics). 68, 963-983.

[161] Lee, Y. and Lee, S. (2019). CUSUM tests for general nonlinear inter-valued GARCHmodels: comparison study. Annals of Institute of Statistical Mathematics. 71, 1033-1057.

[162] Oh, H. and Lee, S. (2019). Modified residual CUSUM test for location-scale timeseries models with heteroscedasticity. Annals of Institute of Statistical Mathematics.71, 1059-1091.

[163] Lee, S., Simos, M. and Cho, M. (2019). Inferential procedures based on the integratedempirical characteristic function. AStA, Advances in Statistical Analysis. 250, 323-336.

[164] Lee, S. (2019). Residual CUSUM of squares test for poisson integer-valued GARCHmodels. Journal of Statistical Computation and Simulation. 89(17), 3182-3195. Jour-nal of Statistical Computation and Simulation. 89(17), 3182-3195.

[165] Kim, H. and Lee, S. (2019). Improved CUSUM monitoring of Markov countingprocess with frequent zeros. Quality and Reliability Engineering International. 35(7),2371-2394.

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[166] Oh, H. and Lee, S. (2019). Parameter change test for location-scale time seriesmodels with heteroscedasticity based on bootstrap. Applied Probability Business andIndustry. 35 (6), 1322–1343.

[167] Lee, S. (2019). Change point test based on Poisson-QMLE. Journal of Mathematicsand Statistics. 15 (1), 250-258

[168] Lee, S. (2019). Cumulative residual entropy-based goodness of fit test for location-scale time series model. In: Kreinovich V., Sriboonchitta S. (eds) Structural Changesand Their Economic Modeling. TES 2019. Studies in Computational Intelligence, vol753. Springer, Cham.

[169] Jimenez-Gamero, M. D., Lee, S. and Simos, M. (2020). Goodness-of-fit tests forparametric specification of GARCH models. Online published in TEST.

[170] Kim, B. and Lee, S. (2020). Robust estimation for general integer-valued time seriesmodels. Online published in Annals of Institute of Statistical Mathematics.

[171] Kim, H and Lee, S. (2020). On residual CUSUM Statistic for PINAR(1) model instatistical design and diagnostic of control chart. Online published in Communicationin Statistics: Simulation and Computation.

[172] Lee, S., Lee, S. and Moon, M. (2020). Hybrid change point detection for timeseries via support vector regression and CUSUM method. To appear in Applied SoftComputing.

Articles in National Journals

[173] Lee, S. (1997). Fixed accuracy confidence set for the autocorrelations of linearprocesses. Korean communications in Statistics. 4, 345-351.

[174] Fakhre-Zakeri, I. and Lee, S. (2001). On the residual empirical distribution functionof stochastic regression with correlated errors. Korean Communications in Statistics.8, 291-297.

[175] Lee, S., Ahn, S., Park, C. and Jeon, J. (2002). Spatial-temporal modeling of roadtraffic data in Seoul. Journal of the Korean Data & Information Science Society. 13,261-270.

[176] Choi, Y., Lee, S. and Lee, S. (2003). Generalized linear model with time series data(in Korean). Korean Journal of Applied Statistics. 16, 365-376.

[177] Na, M. and Lee, S. (2003). Burn-in when repair costs vary with time. Journal of theKorean Society for Quality Management. 31, 142-147.

[178] Kim, J. and Lee, S. (2003). An iterative method for the Cramer-von Mises distanceestimator. The Journal of Basic Sciences Cheju National University. 16, 99-110.

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[179] Kim, E., Ha, J., Jeon, Y. and Lee, S. (2004). Ljung-Box test in unit root AR-ARCHmodel. Korean Communications in Statistics. 11, 323-327.

[180] Na, M. and Lee, S. (2005). Optimal age burn-in and maintaining policy. KoreanSociety for Industrial and Applied Mathematics. 9, 41-28.

[181] Kim, R., Lee, S., Kim, S. and Nam, Y. (2005). A study on developing an optimalsize system for ready-to-wear: based on elementary school girls (in Korean). Journalof the Korean Society of Clothing and Textiles. 8, 1102-1113.

[182] Park, S. and Lee, S. (2006). Simulation study on the scale change test for autoregres-sive models with heavy-tailed innovations. Journal of the Korean Data & InformationScience Society. 17, 1297-1403.

[183] Na,O, Lee, S.J., Lee, S. and Choi, I. (2006). Moving estimates test for jumps intime series models. Communications for Statistical Applications and Methods. 13,205-217.

[184] Park, S. and Lee, S. (2007). Modeling KOSPI 200 data based on GARCH(1,1)parameter change. Journal of the Korean Data & Information Science Society. 18,11-16.

[185] Song, J., Lee, S., and Na, O. (2007). Minimum density power divergence estimatorfor diffusion parameter in discretely observed diffusion processes. Korean Communi-cations in Statistics. 14, 267-280.

[186] Park, S., Lee, S. and Lee, S. (2008). On goodness-of-fit tests based on divergencemeasure. Journal of the Korean Data & Information Science Society. 19, 259-265.

[187] Shim, J., Kim, T., Lee, S. and Hwang, C. (2009). Credibility estimation via kernelmixed effects model. Journal of the Korean Data & Information Science Society. 20,211-218.

[188] Lee, S., Lee, T. and Na, O. (2010). CUSUM of squares test for discretely observedsample from diffusion processes. Journal of the Korean Data & Information ScienceSociety. 21, 179-184.

[189] Na, O., Ko, B. and Lee, S. (2010). CUSUM of squares test for discretely observedsample from multidimensional diffusion processes. Journal of the Korean Data &Information Science Society. 21, 547-554.

[190] Lee, S. and Noh, J. (2010). Value at risk forecasting based on quantile regression forGARCH models. Korean Journal of Applied Statistics. 23, 669-681.

[191] Kim, M. and Lee, S. (2010). The CUSUM test for stochastic volatility models.Journal of the Korean Data and Information Science Society 21, 1305-1310.

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[192] Lee, S., Lee, J. and Noh, J. (2013). Maximum entropy test for infinite order au-toregressive models. Journal of the Korean Data & Information Science Society. 24,637-642.

[193] Lee, S. and Noh, J. (2013). An empirical study on explosive volatility test with possi-bly nonstationary GARCH(1,1) models. Communications for Statistical Applicationsand Methods. 20, 207-215.

[194] Lee, S. and Kim, B. (2013). Dependence structure analysis of KOSPI and NYSEbased on time-varying copula models. Journal of the Korean Data & InformationScience Society. 24, 1477-1488.

[195] Noh, J. and Lee, S. (2013). Forecasting value-at-risk by encompassing CAViaRmodels via information criteria. Journal of the Korean Data and Information ScienceSociety. 24, 1531-1541.

[196] Kim, M. and Lee, S. (2016). Comparison study of semiparametric methods to esti-mate conditional VaR and ES. Korean Journal of Applied Statistics (in Korean). 29,171-180.

[197] Jung, J. and Lee, S. (2016). Comparison study of SARIMA and ARGO models forinfluenza epidemics prediction. Journal of the Korean Data & Information ScienceSociety. 27, 1075-1081.

[198] Chyung, Y. and Lee, S. (2018). An analysis on the predictors of adolescents lifesatisfaction based on quantile regression (in Korean). Journal of the Korean Data &Information Science Society. 29(5), 1215-1225.

[199] Yun. Y. and Lee, S. (2019). Real estate VaR estimation in Seoul and Busan, Korea.Journal of the Korean Data & Information Science Society. 30(2), 469-478.

[200] Jeong, Y. and Lee, S. (2019). Recurrent neural network-adapted nonlinear ARMA-GARCH model with application to S&P 500 index data. Journal of the Korean Data& Information Science Society. 30(5), 1-9.

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