Research Article Bayesian Estimation of Inequality and...
Transcript of Research Article Bayesian Estimation of Inequality and...
Research ArticleBayesian Estimation of Inequality and PovertyIndices in Case of Pareto Distribution Using DifferentPriors under LINEX Loss Function
Kamaljit Kaur, Sangeeta Arora, and Kalpana K. Mahajan
Department of Statistics, Panjab University, Chandigarh 160014, India
Correspondence should be addressed to Kamaljit Kaur; [email protected]
Received 29 August 2014; Accepted 7 January 2015
Academic Editor: Karthik Devarajan
Copyright ยฉ 2015 Kamaljit Kaur et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bayesian estimators of Gini index and a Poverty measure are obtained in case of Pareto distribution under censored and completesetup. The said estimators are obtained using two noninformative priors, namely, uniform prior and Jeffreysโ prior, and oneconjugate prior under the assumption of Linear Exponential (LINEX) loss function. Using simulation techniques, the relativeefficiency of proposed estimators using different priors and loss functions is obtained.The performances of the proposed estimatorshave been compared on the basis of their simulated risks obtained under LINEX loss function.
1. Introduction
The Pareto distribution is a skewed, heavy-tailed distributionthat is used to model the distribution of incomes and otherfinancial variables. It was introduced by Pareto [1] which hasa probability density function of the form
๐ (๐ฅ) ={{{
๐ผ๐๐ผ
๐ฅ๐ผ+1, ๐ โค ๐ฅ < โ; ๐, ๐ผ > 0,
0, otherwise,(1)
and cumulative distribution function is
๐น (๐ฅ) ={{{
1 โ (๐
๐ฅ)๐ผ
, ๐ฅ โฅ ๐,
0, otherwise.(2)
The parameter ๐ in (2) represents the minimum income inthe population under study and assumed to be known, whilethe other parameter ๐ผ is assumed to be unknown.
The average income for Pareto distribution is
๐ =๐๐ผ
(๐ผ โ 1), ๐ผ > 1. (3)
In the context of income inequality and poverty, Gini indexand Poverty measure head count ratio are two most popularindices [2, 3]. Gini index is generally defined as
๐บ = 1 โ twice the area under the Lorenz curve
= 1 โ 2โซ1
0
๐ฟ (๐) ๐๐, 0 โค ๐ โค 1,(4)
where ๐ฟ(๐) = (1/๐) โซ๐
0
๐นโ1(๐ก)๐๐ก is the equation of the Lorenzcurve and ๐ = โซ
1
0
๐นโ1(๐ก)๐๐ก is the mean of the distribution.Equivalently, Gini index can also be defined as
๐บ =ฮ
2๐, (5)
where ฮ = โซโ
0
โซโ
0
|๐ฅ โ ๐ฆ|๐(๐ฅ)๐(๐ฆ)๐๐ฅ ๐๐ฆ is population Ginimean difference.
The Poverty index head count ratio ๐0is simply the count
of the number of households whose incomes are below thepoverty line divided by the total population. In terms ofcontinuous distribution,
๐0= โซ๐ค0
0
๐ (๐ฆ) ๐๐ฆ = ๐น (๐ค0) , (6)
where, ๐ค0(> ๐) is called Poverty Line.
Hindawi Publishing CorporationAdvances in StatisticsVolume 2015, Article ID 964824, 10 pageshttp://dx.doi.org/10.1155/2015/964824
2 Advances in Statistics
In case of Pareto distribution,Gini index (๐บ) [4, 5] is givenby
๐บ =1
(2๐ผ โ 1), ๐ผ >
1
2, (7)
and Poverty measure (๐0) is
๐0= ๐น (๐ค
0)
= 1 โ (๐
๐ค0
)๐ผ
= 1 โ ๐๐ผ0,
(8)
where, ๐ค0(> ๐) and ๐
0= (๐/๐ค
0).
Thus, ๐ค0is per capita annual income representing a
minimum acceptable standard of living and ๐0represents the
proportion of population having income equal to or less than๐ค0.The estimation of Gini index (๐บ) and Poverty measure
(๐0) and the associated inference using classical approach
(parametric and nonparametric) is available in literature [5โ8]. However, in the Bayesian setup, this has not evoked theinterest of many researchers [9, 10]. In the present paper,our focus will be on the estimation of inequality and povertyindices in the Bayesian setup.
When the Bayesian method is used, the choice of appro-priate prior distribution plays an important role, whichmay be categorized as informative, noninformative, andconjugate priors [11, 12]. In the present paper, three priors(two noninformative priors and one conjugate prior) are usedto estimate shape parameter, Gini index, Average income, andPovertymeasure.The two noninformative priors areUniformprior and Jeffreysโ prior, while conjugate prior is chosen asTruncated Erlang distribution.
In Bayesian estimation, the criterion for good estimatorsfor the parameters of interest is the choice of appropriate lossfunction. In Bayesian estimation, two types of loss functionscommonly used are Squared error loss function (SELF) andLinear exponential (LINEX) loss function. The simplest typeof loss function is squared error, which is also referred to asquadratic loss is given as
๐ฟ (๐) = (๐ โ ๐)2
, (9)
where ๐ is the estimator of ๐.The usual squared error loss function is symmetrical and
associates equal importance to the losses due to overestima-tion and underestimation of equal magnitude. However, sucha restriction may be impractical; for example, in estimationof shape parameter of Classical Pareto distribution, theoverestimation and underestimation may not be of equalimportance as over estimate of shape parameter gives anunder-estimate of inequality index which seems to be moreserious as compared to under estimate of shape parameterbecause we are often interested in reducing income inequalityindex. This leads one to think that an asymmetrical lossfunction be considered for estimation of shape parame-ter which associates greater importance to overestimation.Anumber of asymmetrical loss functions have been proposed
in statistical literature [13โ16]. Varian [16] proposed a usefulasymmetrical loss function known as Linear exponential(LINEX) loss function which is given as
๐ฟ (๐ โ ๐) = ๐๐(ฬ๐โ๐) โ ๐ (๐ โ ๐) โ 1, ๐ ฬธ= 0. (10)
The posterior expectation of the LINEX loss function (10) is
๐ธ (๐ฟ (๐ โ ๐)) = ๐๐ฬ๐๐ธ (๐โ๐๐) โ ๐ (๐ โ ๐ธ (๐)) โ 1, (11)
where ๐ธ(โ ) denotes posterior expectation with respect to theposterior density of ๐.
By a result of Zellner [17] the Bayes estimator of ๐ denotedby ๐ under the LINEX loss function is the value whichminimizes posterior expectation and is given by
๐ = โ1
๐ln [๐ธ (๐โ๐๐)] , (12)
provided that the expectation ๐ธ(๐โ๐๐) exists and is finite [18].In Figures 1(a) and 1(b), values of ๐ฟ(๐) are plotted for the
selected values of ๐ for ๐ = 1 and ๐ = โ1. It is seen that, for๐ = 1, the function is quite asymmetricwith a value exceedingthe target being more serious than a value below the target.But, for ๐ = โ1, the function is also quite asymmetric with avalue below the target value being more serious than a valueexceeding the target.
For small value of ๐, the LINEX loss function can beexpanded by Taylorโs series expansion as
exp (๐ (๐ โ ๐)) โ ๐ (๐ โ ๐) โ 1
=โ
โ๐=0
๐๐ (๐ โ ๐)๐
๐!โ ๐ (๐ โ ๐) โ 1
=โ
โ๐=2
๐๐ (๐ โ ๐)๐
๐!
โ๐2 (๐ โ ๐)
2
2.
(13)
Thus, the LINEX loss function is approximately equal tosquared error loss function for small values of b (seeFigure 1(c)).
This loss function has been considered by Zellner [17],Basu and Ebrahimi [19], and Afify [20] for different distri-butions.
In the present study, LINEX loss function is used forestimating the shape parameter, Gini index, Mean income,and a Poverty measure in the context of Pareto distributionusing noninformative priors (Uniform prior and Jeffreysโprior) and one conjugate prior (Truncated Erlang distribu-tion) along with some assumptions regarding the sampledpopulation. Bayesian approach with prior and posteriordistributions along with sampling schemes in the contextof Pareto distribution is given in Section 2. In Section 3,Bayesian estimators of shape parameter, Gini index, Meanincome, and Poverty measure using different priors under
Advances in Statistics 3
858.5 859.0 859.5 860.0 860.5 861.0
0.0
0.5
1.0
1.5
L(๐)
๐
(a)
858.5 859.0 859.5 860.0 860.5 861.0
0.0
0.5
1.0
1.5
L(๐)
๐
(b)
858.5 859.0 859.5 860.0 860.5 861.0
0.000
0.002
0.004
0.006
L(๐)
๐
(c)
Figure 1: (a) LINEX Loss function when ๐ = 859.5 and ๐ = 1. (b) LINEX Loss function when ๐ = 859.5 and ๐ = โ1. (c) LINEX Loss functionwhen ๐ = 859.5 and ๐ = 0.1.
the assumption of LINEX loss function are obtained. Finally,in Section 4, simulation is done to compare the efficiencyof three different approaches using three priors and lossfunctions. The robustness of the hyperparameters is given inSection 4.1 through simulation study. Section 5 presents theconclusion of the study.
2. Preliminary about Sampling Scheme, Priors,and Posterior Densities
The Bayesian analysis of the Pareto distribution (2) is basedon the following censored sampling scheme on personalincome data. It is assumed that annual incomes of the ๐persons are under study but exact figures ๐ฅ
1, ๐ฅ2, ๐ฅ3, . . . , ๐ฅ
๐are
available only for those ๐ individuals whose annual incomedoes not exceed a prescribed annual income๐ค
0(> ๐), and for
the remaining (๐โ๐) individuals, the exact income figures are
unknown but we do know that their annual income exceedthe prescribed figure๐ค
0. Before the arrival of the sample data
on personal incomes, ๐ is predetermined but not ๐, whichis a random. This censoring scheme used is referred as rightcensored sampling scheme.
The likelihood function ๐ฟ(๐ผ) for complete sample in caseof Pareto distribution [4] is
๐ฟ (๐ผ) = ๐ผ๐๐๐๐ผ(๐
โ๐=1
๐ฅ๐)
โ(๐ผ+1)
. (14)
In case of censored data, the likelihood function for anydistribution [21] is
๐ฟ (๐; ๐ผ) =๐!
(๐ โ ๐)!
๐
โ๐=1
๐ (๐ฅ; ๐ผ) [1 โ ๐น (๐ฅ; ๐ผ)]๐โ๐ . (15)
4 Advances in Statistics
The likelihood function for Pareto distribution in censoredsample is
๐ฟ (๐ผ) =๐ผ๐๐๐๐ผ๐(๐โ๐)๐ผ
๐ค
(โ๐
๐=1๐ฅ๐)(๐ผ+1)
โ ๐ผ๐๐โ๐ผ๐๐ค , ๐ผ โ (๐ฟ,โ) , (16)
where ๐๐ค= ln(๐โ๐๐
๐ค) is product income statistics [22] and
๐๐ค= ๐ค๐โ๐(โ
๐
๐=1๐ฅ๐).
Bayes estimators of Gini index and Average incomewill not be convergent in the interval [0, 1/2] and [0, 1],respectively, and the method will fail to work. Hence, thisdifficulty is removed by assuming ๐ฟ > 1, to obtain differentBayes estimators.
The prior and posterior densities for noninformativepriors (Uniform prior and Jeffreysโ prior) and conjugate priorare explained below.(i) Uniform Prior. In practice, the informative priors arenot always available; for such situations, the use of nonin-formative priors is recommended. One of the most widelyused noninformative prior, due to Laplace [23], is a uniformprior. Therefore, the uniform prior has been assumed for theestimation of the shape parameter of the Pareto distribution.
Uniform prior for ๐ผ is๐๐ข(๐ผ) โ 1. (17)
Combine likelihood function (16) with the prior density (17)by using Bayes theorem to obtain the posterior density as
๐โ๐ข(๐ผ) =
๐ฟ (๐ผ) โ ๐ (๐ผ)
โซโ
๐ฟ
๐ฟ (๐ผ) โ ๐ (๐ผ) ๐๐ผ
=(๐๐ค)๐+1
ฮ (๐ + 1, ๐๐ค๐ฟ)๐ผ๐๐โ๐ผ๐๐ค ,
(18)
where ฮ(๐, ๐ฆ) = โซโ
๐ฆ
๐ข๐โ1๐โ๐ข๐๐ข, ๐ฆ > 0 is the upper incompletegamma function and posterior density ๐โ
๐ข(๐ผ) is left truncated
Gamma distribution.(ii) Jeffreysโ Prior. Another noninformative prior has beensuggested by Jeffreys [24] which is frequently used in situa-tions where one does not have much information about theparameters. This is defined as the distribution of the param-eters proportional to the square root of the determinants ofthe Fisher information matrix, that is, ๐(๐ผ) โ โ๐ผ(๐ผ), where๐ผ(๐ผ) = โ๐ธ[(๐2/๐๐ผ2) log ๐ฟ(๐ผ | ๐ฅ)] is Fisherโs information ofthe given distribution. In case of Pareto distribution,
๐๐(๐ผ) โ
โ๐
๐ผ. (19)
A motivation for Jeffreysโ prior is that Fisherโs information(๐ผ(๐ผ)) is an indicator of the amount of information broughtby the model (observations) about ๐ผ.
The posterior density is obtained as
๐โ๐(๐ผ) =
(๐๐ค)๐
ฮ (๐, ๐๐ค๐ฟ)๐ผ๐โ1๐โ๐ผ๐๐ค , (๐ฟ โค ๐ผ < โ) , (20)
which is left truncated Gamma distribution.
Note: Extension of Jeffreysโ Prior. Jeffreysโ prior is a particularcase of extension of Jeffreysโ prior proposed by Al-Kutubi andIbrahim [25], defined as
๐ (๐ผ) โ [๐ผ (๐ผ)]๐ , (21)
where ๐ is a positive constant. For ๐ = 0.5, it reduces toJeffreysโ prior.
In case of Pareto distribution, this prior is
๐๐(๐ผ) โ (
๐
๐ผ2)๐
. (22)
Theposterior distribution by using extension to Jeffreysโ prioris obtained as
๐โ๐(๐ผ) =
(๐๐ค)๐โ2๐+1
ฮ (๐ โ 2๐ + 1, ๐๐ค๐ฟ)๐ผ๐โ2๐๐โ๐ผ๐๐ค , (๐ฟ โค ๐ผ < โ) .
(23)
(iii) Conjugate Prior. The conjugate prior was introducedby Raiffa and Schlaifer [26], where the prior and posteriordistributions are from the same family, that is, the form ofthe posterior density has the same distributional form as theprior distribution. For the existence of Gini index and Meanincome for the Pareto distribution, wemust take into accounta truncated prior distribution since the random variable ๐ผ isdefined in (๐ฟ,โ), where the constant ๐ฟ > 1 is assumed to beknown.
Let ๐ผ have Truncated Erlang distribution [22]
๐๐(๐ผ) =
(๐ฝ)๐
ฮ (๐, ๐ฟ๐ฝ)๐ผ๐โ1๐โ๐ฝ๐ผ
โผ TED (๐ฝ, ๐; ๐ฟ) ,
(๐ฟ < ๐ผ < โ, ๐ฟ > 1, ๐ฝ > 0, ๐ = 1, 2, . . .) ,
(24)
where ๐ฝ and ๐ are the hyperparameters.The posterior density for ๐ผ is
๐โ๐(๐ผ) =
(๐ฝ + ๐๐ค)๐+1
ฮ (๐ + ๐, (๐ฝ + ๐๐ค) ๐ฟ)
๐ผ๐+๐โ1๐โ(๐ฝ+๐๐ค)๐ผ
โผ TED ((๐ฝ + ๐๐ค) , (๐ + ๐) ; ๐ฟ) .
(25)
The posterior density (๐โ๐(๐ผ)) follows Truncated Erlang dis-
tribution with parameters (๐ฝ + ๐๐ค) and (๐ + ๐).
Advances in Statistics 5
3. Bayesian Estimation under LinearExponential (LINEX) Loss Function UsingDifferent Priors
3.1. Bayesian Estimators Using Uniform Prior. Bayesian esti-mator ๏ฟฝฬ๏ฟฝ of ๐ผ using uniform prior (17) and posterior density(18), under the assumption of the LINEX loss function (ref.(12)) is obtained as
๏ฟฝฬ๏ฟฝ๐ข= โ
1
๐log๐ธ [๐โ๐๐ผ] ,
๐ธ [๐โ๐๐ผ] = โซโ
๐ฟ
๐โ๐๐ผ๐โ๐ข(๐ผ) ๐๐ผ
=(๐๐ค)๐+1
ฮ (๐ + 1, ๐๐ค๐ฟ)
โซโ
๐ฟ
๐ผ๐๐โ(๐+๐๐ค)๐ผ๐๐ผ
=(๐๐ค)๐
ฮ (๐ + 1, ๐๐ค๐ฟ)
ฮ (๐ + 1, (๐ + ๐๐ค) ๐ฟ)
(๐ + ๐๐ค)๐
=ฮ (๐ + 1, (๐ + ๐
๐ค) ๐ฟ)
ฮ (๐ + 1, ๐๐ค๐ฟ)
(๐๐ค
๐ + ๐๐ค
)๐+1
.
(26)
Therefore,
๏ฟฝฬ๏ฟฝ๐ข= โ
1
๐log(
ฮ (๐ + 1, (๐ + ๐๐ค) ๐ฟ)
ฮ (๐ + 1, ๐๐ค๐ฟ)
(๐๐ค
๐ + ๐๐ค
)๐+1
) . (27)
The Bayes estimator ๐บ of ๐บ, using uniform prior is
๐บ๐ข= โ
1
๐log๐ธ [๐โ๐๐บ] ,
๐ธ [๐โ๐๐บ] = ๐ธ [๐โ๐/(2๐ผโ1)]
=(๐๐ค)๐+1
ฮ (๐ + 1, ๐๐ค๐ฟ)
โซโ
๐ฟ
๐ผ๐๐โ(๐/(2๐ผโ1)+๐ผ๐๐ค)๐๐ผ
(28)
putting ๐ก = 2๐ผ โ 1
= (๐๐ค
2)๐+1
๐โ๐๐ค/2
ฮ (๐ + 1, ๐๐ค๐ฟ)
โ ๐
โ๐=0
(๐๐)โซโ
2๐ฟโ1
๐ก๐๐โ(๐/๐ก+๐ก๐๐ค/2)๐๐ก
(By Binomial expansion)
=(๐๐ค)๐โ1
๐โ๐๐ค/2
2๐ฮ (๐ + 1, ๐๐ค๐ฟ)
โ ๐
โ๐=0
(๐๐)(
2๐
๐๐ค
)(๐+1)/2
๐พ๐+1
(โ2๐๐๐ค)
(29)
(using formula (9) of 3.471, page 368 of Gradshteyn andRyzhik [27] โซ
โ
0
๐ฅ]โ1๐โ๐ฝ/๐ฅโ๐พ๐ฅ๐๐ฅ = 2(๐ฝ/๐พ)]/2๐พ](2โ๐ฝ๐พ),[Re๐ฝ > 0, Re ๐พ > 0] where๐พ](โ ) is modified Bessel functionof third kind).
Thereby,
๐บ๐ข= โ
1
๐log(
(๐๐ค)๐โ1
๐โ๐๐ค/2
2๐ฮ (๐ + 1, ๐๐ค๐ฟ)
โ ๐
โ๐=0
(๐๐)(
2๐
๐๐ค
)(๐+1)/2
๐พ๐+1
(โ2๐๐๐ค)) .
(30)
The Bayes estimator ๏ฟฝฬ๏ฟฝ of ๐, using uniform prior is
๏ฟฝฬ๏ฟฝ๐ข= โ
1
๐log๐ธ [๐โ๐๐] ,
๐ธ [๐โ๐๐] = ๐ธ [๐โ๐๐๐ผ/(๐ผโ1)]
=(๐๐ค)๐+1
ฮ (๐ + 1, ๐๐ค๐ฟ)
โซโ
๐ฟ
๐ผ๐๐โ(๐๐๐ผ/(๐ผโ1)+๐ผ๐๐ค)๐๐ผ
putting ๐ก = ๐ผ โ 1
=(๐๐ค)๐+1
๐โ(๐๐+๐๐ค)
ฮ (๐ + 1, ๐๐ค๐ฟ)
โ ๐
โ๐=0
(๐๐)โซโ
๐ฟโ1
๐ก๐๐โ(๐๐/๐ก+๐ก๐๐ค)๐๐ก
=(๐๐ค)๐+1
๐โ(๐๐+๐๐ค)
ฮ (๐ + 1, ๐๐ค๐ฟ)
โ ๐
โ๐=0
(๐๐) 2(
๐๐
๐๐ค
)(๐+1)/2
๐พ๐+1
(2โ๐๐๐๐ค)
(31)
(using formula (9) of 3.471, page 368 of Gradshteyn andRyzhik [27] โซ
โ
0
๐ฅ]โ1๐โ๐ฝ/๐ฅโ๐พ๐ฅ๐๐ฅ = 2(๐ฝ/๐พ)]/2๐พ](2โ๐ฝ๐พ),[Re๐ฝ > 0, Re ๐พ > 0] where๐พ](โ ) is modified Bessel functionof third kind)
๏ฟฝฬ๏ฟฝ๐ข= โ
1
๐log(
(๐๐ค)๐+1
๐โ(๐๐+๐๐ค)
ฮ (๐ + 1, ๐๐ค๐ฟ)
โ ๐
โ๐=0
(๐๐) 2(
๐๐
๐๐ค
)(๐+1)/2
๐พ๐+1
(2โ๐๐๐๐ค)) .
(32)
The Bayes estimator ๏ฟฝฬ๏ฟฝ0of ๐0, using uniform prior, is
๏ฟฝฬ๏ฟฝ0๐ข= โ
1
๐log๐ธ [๐โ๐๐0] ,
๐ธ [๐โ๐๐0] = ๐ธ [๐โ๐(1โ๐๐ผ
0)]
6 Advances in Statistics
=(๐๐ค)๐+1
ฮ (๐ + 1, ๐๐ค๐ฟ)
โซโ
๐ฟ
๐โ๐(1โ๐๐ผ
0)๐ผ๐๐โ๐ผ๐๐ค๐๐ผ,
๏ฟฝฬ๏ฟฝ0๐ข= โ
1
๐log(
(๐๐ค)๐+1
ฮ (๐ + 1, ๐๐ค๐ฟ)
โซโ
๐ฟ
๐โ๐(1โ๐๐ผ
0)๐ผ๐โ1๐โ๐ผ๐๐ค๐๐ผ) .
(33)
3.2. Bayesian Estimators Using Jeffreysโ Prior. In case ofJeffreysโ prior (19) and using posterior density (20), theBayesian estimators of ๐ผ,๐บ,๐, and ๐
0under the assumption
of the LINEX loss function are obtained as follows:
๏ฟฝฬ๏ฟฝ๐= โ
1
๐log๐ธ [๐โ๐๐ผ]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐๐ผ๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
ฮ (๐, (๐ + ๐๐ค) ๐ฟ)
ฮ (๐, ๐๐ค๐ฟ)
(๐๐ค
๐ + ๐๐ค
)๐
) ,
๐บ๐= โ
1
๐log๐ธ [๐โ๐๐บ]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐/(2๐ผโ1)๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
(๐๐ค)๐
๐โ๐๐ค/2
2๐โ1ฮ (๐, ๐๐ค๐ฟ)
โ ๐โ1
โ๐=0
(๐ โ 1๐
)(2๐
๐๐ค
)(๐+1)/2
๐พ๐+1
(โ2๐๐๐ค)) ,
๏ฟฝฬ๏ฟฝ๐= โ
1
๐log๐ธ [๐โ๐๐]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐๐๐ผ/(๐ผโ1)๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
(๐๐ค)๐
๐โ(๐๐+๐๐ค)
ฮ (๐, ๐๐ค๐ฟ)
โ ๐โ1
โ๐=0
(๐ โ 1๐
) 2(๐๐
๐๐ค
)(๐+1)/2
โ ๐พ๐+1
(2โ๐๐๐๐ค)) ,
๏ฟฝฬ๏ฟฝ0๐= โ
1
๐log๐ธ [๐โ๐๐0]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐(1โ๐๐ผ
0)๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
(๐๐ค)๐
ฮ (๐, ๐๐ค๐ฟ)
โซโ
๐ฟ
๐โ๐(1โ๐๐ผ
0)๐ผ๐โ1๐โ๐ผ๐๐ค๐๐ผ) .
(34)
Note. The expression for extension of Jeffreysโ prior can beobtained with some modifications in Jeffreysโ prior and arelisted below:
๏ฟฝฬ๏ฟฝ๐= โ
1
๐log(
ฮ (๐ โ 2๐ + 1, (๐ + ๐๐ค) ๐ฟ)
ฮ (๐ โ 2๐ + 1, ๐๐ค๐ฟ)
(๐๐ค
๐ + ๐๐ค
)๐โ2๐+1
) ,
๐บ๐= โ
1
๐log(
(๐๐ค)๐โ2๐+1
๐โ๐๐ค/2
2๐โ2๐ฮ (๐ โ 2๐ + 1, ๐๐ค๐ฟ)
โ ๐โ2๐
โ๐=0
(๐ โ 2๐๐
)(2๐
๐๐ค
)(๐+1)/2
๐พ๐+1
(โ2๐๐๐ค)),
๏ฟฝฬ๏ฟฝ๐= โ
1
๐log(
(๐๐ค)๐โ2๐+1
๐โ(๐๐+๐๐ค)
ฮ (๐ โ 2๐ + 1, ๐๐ค๐ฟ)
โ ๐โ2๐
โ๐=0
(๐ โ 2๐๐
) 2(๐๐
๐๐ค
)(๐+1)/2
โ ๐พ๐+1
(2โ๐๐๐๐ค)) ,
๏ฟฝฬ๏ฟฝ0๐= โ
1
๐log(
(๐๐ค)๐โ2๐+1
ฮ (๐ โ 2๐ + 1, ๐๐ค๐ฟ)
โ โซโ
๐ฟ
๐โ๐(1โ๐๐ผ
0)๐ผ๐โ2๐๐โ๐ผ๐๐ค๐๐ผ) .
(35)
3.3. Bayesian Estimators Using Conjugate Prior. Using theBayesian posterior density (25), the Bayes estimators of ๐ผ, ๐บ,๐, and ๐
0, under the assumption of the LINEX loss function
are
๏ฟฝฬ๏ฟฝ๐= โ
1
๐log๐ธ [๐โ๐๐ผ]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐๐ผ๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
ฮ (๐ + ๐, (๐ + ๐ฝ + ๐๐ค) ๐ฟ)
ฮ (๐ + ๐, (๐ฝ + ๐๐ค) ๐ฟ)
(๐ฝ + ๐
๐ค
๐ + ๐ฝ + ๐๐ค
)๐+๐
) ,
๐บ๐= โ
1
๐log๐ธ [๐โ๐๐บ]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐/(2๐ผโ1)๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
(๐ฝ + ๐๐ค)๐+๐
๐โ(๐ฝ+๐๐ค)/2
2๐+๐โ1ฮ (๐ + ๐, (๐ฝ + ๐๐ค) ๐ฟ)
โ ๐+๐โ1
โ๐=0
(๐ + ๐ โ 1
๐)(
2๐
๐ฝ + ๐๐ค
)
(๐+1)/2
โ ๐พ๐+1
(โ2๐๐๐ค)) ,
Advances in Statistics 7
๏ฟฝฬ๏ฟฝ๐= โ
1
๐log๐ธ [๐โ๐๐]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐๐๐ผ/(๐ผโ1)๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
(๐ฝ + ๐๐ค)๐+๐
๐โ(๐๐+๐ฝ+๐๐ค)
ฮ (๐ + ๐, (๐ฝ + ๐๐ค) ๐ฟ)
โ ๐+๐โ1
โ๐=0
(๐ + ๐ โ 1
๐) 2(
๐๐
๐ฝ + ๐๐ค
)
(๐+1)/2
โ ๐พ๐+1
(2โ๐๐๐๐ค)) ,
๏ฟฝฬ๏ฟฝ0๐= โ
1
๐log๐ธ [๐โ๐๐0]
= โ1
๐log(โซ
โ
๐ฟ
๐โ๐(1โ๐๐ผ
0)๐โ๐(๐ผ) ๐๐ผ)
= โ1
๐log(
(๐ฝ + ๐๐ค)๐+๐
ฮ (๐ + ๐, (๐ฝ + ๐๐ค) ๐ฟ)
โ โซโ
๐ฟ
๐โ๐(1โ๐๐ผ
0)๐ผ๐+๐โ1๐โ(๐ฝ+๐๐ค)๐ผ๐๐ผ) .
(36)
Note: Case of Complete Sample. The Bayesian estimatorsfor complete sample can be obtained using noninformativepriors and conjugate prior by simply substituting ๐ = ๐ in theabove estimators.
4. Simulation Study
In order to assess the statistical performance of these esti-mators of shape parameter, Gini index, Mean income, andPoverty measure using LINEX loss function, a simulationstudy is conduced. The estimated losses are computed usinggenerated random samples from Pareto distribution of dif-ferent sizes. These estimated losses are computed for samplesizes ๐ = 20 (20) 100, ๐ผ = 2.5 (1) 4.5, ๐ = 1, ๐ฟ = 1.5,and ๐ = 450. The value of ๐ค
0= 859.6 should be taken
from Poverty line given by the Government of India in 2009-10 for urban people. For the conjugate prior, the values ofhyperparameter are taken as ๐ฝ = 0.5, ๐ = 2; ๐ฝ = 2, and๐ = 2. The estimated losses of ๐ผ, ๐บ, ๐, and ๐
0with LINEX
loss function by using noninformative (Uniform prior andJeffreysโ prior) and conjugate priors are tabulated in Tables1, 2, 3, and 4, respectively.
It is observed from the above simulation study (ref. Tables1, 2, 3, and 4) that
(i) Bayesian estimators with conjugate prior (hyperpa-rameter ๐ฝ = 0.5, ๐ = 2) perform better as compared tononinformative priors as it has smaller estimated lossfor ๐ผ, ๐บ,๐, and ๐
0;
(ii) in case of noninformative priors, Jeffreysโ prior hasless estimated loss than uniform prior, which impliesthat Bayesian methods with Jeffreysโ prior are better;
Table 1: Estimated loss functions for ๐ผ using LINEX loss function.
๐ ๐ผUniformprior
Jeffreyโsprior
Conjugate prior๐ฝ = 0.5๐ = 2
๐ฝ = 2๐ = 2
202.5 0.200543 0.173893 0.112013 0.1132993.5 0.423936 0.357678 0.281125 0.3718234.5 0.719781 0.456351 0.311154 0.710794
402.5 0.110843 0.077269 0.050112 0.0728723.5 0.207535 0.204212 0.145707 0.1743984.5 0.324085 0.228739 0.207738 0.344289
602.5 0.065696 0.061891 0.058858 0.0593363.5 0.135812 0.104322 0.102511 0.1235644.5 0.283127 0.211419 0.149228 0.224148
802.5 0.048582 0.052477 0.044407 0.0452433.5 0.094729 0.094126 0.081215 0.0898614.5 0.146575 0.140906 0.126948 0.163061
1002.5 0.047068 0.040324 0.034990 0.0383363.5 0.072414 0.071366 0.065080 0.0705024.5 0.112283 0.104459 0.099383 0.131260
Table 2: Estimated loss functions for G using LINEX loss function.
๐ ๐ผUniformprior
Jeffreyโsprior
Conjugate prior๐ฝ = 0.5๐ = 2
๐ฝ = 2๐ = 2
202.5 0.003944 0.0031157 0.002672 0.0573223.5 0.000849 0.0007378 0.000700 0.0167334.5 0.000671 0.0005303 0.000463 0.009637
402.5 0.001503 0.0011873 0.000963 0.0083623.5 0.000642 0.0005590 0.000516 0.0037824.5 0.000314 0.0002975 0.000197 0.002782
602.5 0.000811 0.0007397 0.000692 0.0063733.5 0.000415 0.0003852 0.000319 0.0017834.5 0.000200 0.0001726 0.000159 0.000873
802.5 0.000687 0.0006286 0.000586 0.0026373.5 0.000298 0.0002746 0.000189 0.0009784.5 0.000141 0.0001403 0.000116 0.000512
1002.5 0.000611 0.0005395 0.000483 0.0010323.5 0.000231 0.0002250 0.000102 0.0008224.5 0.000115 0.0001073 0.000083 0.000421
(iii) a change in the value of ๐ฝ on higher side does resultin an increase in the loss; the loss remains unaffectedby the change in the value of ๐.
In Table 5 simulation study is taken to find estimated lossfor ๐ผ, ๐บ, ๐, and ๐
0under the assumptions of SELF using
different priors by considering small as well as large samplesfor comparisons purpose with the LINEX loss function.
From Table 5 and its comparison with LINEX loss func-tion (ref. Tables 1, 2, 3, and 4), it is observed that LINEXloss function gives smaller loss in comparison with SELF for
8 Advances in Statistics
Table 3: Estimated loss functions forM using LINEX loss function.
๐ ๐ผUniformprior
Jeffreyโsprior
Conjugate prior๐ฝ = 0.5๐ = 2
๐ฝ = 2๐ = 2
202.5 0.073957 0.0657402 0.026145 0.0564653.5 0.061835 0.0558135 0.015994 0.0467434.5 0.056649 0.0418561 0.012289 0.035673
402.5 0.073204 0.0555914 0.025888 0.0436743.5 0.060616 0.0466542 0.016802 0.0403014.5 0.055089 0.0435518 0.013802 0.031533
602.5 0.072393 0.0458580 0.026035 0.0393733.5 0.059386 0.0376830 0.017845 0.0363734.5 0.053528 0.0352241 0.015377 0.025377
802.5 0.071778 0.0360502 0.026361 0.0300123.5 0.058222 0.0286558 0.018818 0.0297334.5 0.051894 0.0267040 0.016845 0.020345
1002.5 0.071070 0.0263228 0.020575 0.0279733.5 0.057185 0.0196096 0.019812 0.0287324.5 0.030343 0.0183061 0.018161 0.019637
Table 4: Estimated loss functions for ๐0using LINEX loss function.
๐ ๐ผUniformprior
Jeffreyโsprior
Conjugate prior๐ฝ = 0.5๐ = 2
๐ฝ = 2๐ = 2
202.5 0.003918 0.0016639 0.0015042 0.00373603.5 0.007714 0.0012619 0.0011718 0.00335104.5 0.006892 0.0006375 0.0006100 0.0033324
402.5 0.003030 0.0012092 0.0011452 0.00135733.5 0.001033 0.0007198 0.0007159 0.00162994.5 0.001099 0.0003325 0.0003235 0.0011149
602.5 0.002237 0.0009517 0.0008915 0.00112493.5 0.001652 0.0004774 0.0004483 0.00089254.5 0.001019 0.0002165 0.0002040 0.0005813
802.5 0.001769 0.0007372 0.0007191 0.00090473.5 0.001163 0.0003889 0.0003795 0.00063154.5 0.000659 0.0001677 0.0001622 0.0003924
1002.5 0.001009 0.0006512 0.0005704 0.00071863.5 0.000465 0.0002854 0.0002770 0.00042694.5 0.000287 0.0001354 0.0001240 0.0002843
noninformative priors and conjugate prior for small as wellas large sample sizes. When sample size increases estimatedloss decreases in all cases.
4.1. Choice of Hyperparameters. Sinha and Howlader [28]suggested that a Bayes estimate is robust with respect to itshyperparameter if it leads to a high (min /max) index of theestimate for the varying values of those hyperparameter. Tocheck results, simulations are done by taking different values
Table 5: Estimated loss functions for ๐ผ, G, M, and ๐0using different
priors under the assumptions of SELF.
๐ ๐ผUniformprior
Jeffreyโsprior
Conjugate prior๐ฝ = 0.5๐ = 2
๐ฝ = 2๐ = 2
For๐ผ
402.5 0.198417 0.188773 0.105229 0.1490433.5 0.545553 0.315654 0.301779 0.3030944.5 0.636095 0.546807 0.511984 0.662855
1002.5 0.081056 0.080694 0.065290 0.0722333.5 0.178339 0.192881 0.138684 0.1395104.5 0.261753 0.299142 0.231038 0.215135
For๐บ
402.5 0.002541 0.002135 0.001879 0.0534373.5 0.001215 0.001071 0.001055 0.0336834.5 0.000989 0.000629 0.000222 0.026677
1002.5 0.001347 0.001311 0.001054 0.0113183.5 0.000604 0.000408 0.000407 0.0069674.5 0.000228 0.000236 0.000165 0.005405
For๐
402.5 0.085215 0.075152 0.061571 0.0972153.5 0.092519 0.085051 0.070570 0.1023104.5 0.157210 0.115720 0.095721 0.105721
1002.5 0.105721 0.097121 0.050712 0.0987213.5 0.097215 0.070125 0.033710 0.0597134.5 0.080712 0.052325 0.092530 0.082173
For๐0
402.5 0.003513 0.004420 0.003916 0.0051923.5 0.001382 0.003596 0.002156 0.0049214.5 0.001224 0.001993 0.001057 0.003051
1002.5 0.001152 0.001907 0.001805 0.0019823.5 0.000538 0.000914 0.000705 0.0015724.5 0.000260 0.000896 0.000679 0.000971
of hyperparameter and keeping ๐ผ and ๐ fixed (ref. Tables 6and 7).
The ratio (min /max) in case of both Gini index andPoverty measure is close to 1 for different combinations of๐ and ๐ฝ indicating thereby the Bayes estimates are robustwith respect to hyperparameters, which justifies the use ofhyperparameters in simulation study.
5. Conclusion
The simulation study as carried out in Section 4 suggests thatBayesian estimators using conjugate prior (hyperparameter๐ฝ = 0.5, ๐ = 2) perform better than two noninformativepriors (Uniform prior and Jeffreysโ prior) in general. It is alsoobserved that LINEX loss function results in smaller loss thanthe SELF for both small and large samples irrespective of thechoice of the priors taken for the Bayesian estimators. Hence,the combinations of conjugate prior and LINEX loss results insmaller loss than the choice of other two priors and squarederror loss function. One can further infer that as sample sizeincreases the expected loss function decreases for all cases.
Advances in Statistics 9
Table 6: Bayes estimate of Gini index using conjugate prior (๐ = 100, ๐ผ = 3.5).
๐ฝ๐
(Min /Max) | ๐ฝ1 2 3 4 5
0.5 0.21247 0.22623 0.24944 0.23980 0.24143 0.8531 0.23816 0.25030 0.22015 0.25817 0.23342 0.8521.5 0.20394 0.22034 0.21269 0.23569 0.22392 0.8652 0.22687 0.24029 0.22722 0.26901 0.25348 0.8432.5 0.21976 0.23529 0.25022 0.24789 0.26048 0.845(Min /Max) | ๐ 0.856 0.880 0.850 0.879 0.859
Table 7: Bayes estimate of Poverty measure using conjugate prior (๐ = 100, ๐ผ = 3.5).
๐ฝ๐
(Min /Max) | ๐ฝ1 2 3 4 5
0.5 0.89102 0.89271 0.89536 0.89844 0.89987 0.9981 0.88525 0.88800 0.89170 0.89269 0.89549 0.9891.5 0.88163 0.88560 0.88582 0.88885 0.89141 0.9892 0.87639 0.87720 0.88162 0.88555 0.88619 0.9882.5 0.87005 0.87451 0.87786 0.87947 0.88246 0.985(Min /Max) | ๐ 0.976 0.979 0.980 0.978 0.980
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper.
Acknowledgments
The authors are thankful to the anonymous referees and theeditor for their valuable suggestions and comments.
References
[1] V. Pareto,CoursDโ Economic Politique Paris, Rouge and cie, 1897.[2] C. Gini, Variability and Mutabiltity, C. Cuppini, Bologna, Italy,
1912.[3] J. Foster, J. Greer, and E. Thorbecke, โA class of decomposable
poverty measures,โ Econometrica, vol. 52, no. 3, pp. 761โ766,1984.
[4] B. C. Arnold and S. J. Press, โBayesian inference for Paretopopulations,โ Journal of Econometrics, vol. 21, no. 3, pp. 287โ306,1983.
[5] T. S. Moothathu, โSampling distributions of Lorenz curve andGini index of the Pareto distribution,โ Sankhya (Statistics),Series B, vol. 47, no. 2, pp. 247โ258, 1985.
[6] P. K. Sen, โTheharmonicGini coefficient and affluence indexes,โMathematical Social Sciences, vol. 16, no. 1, pp. 65โ76, 1988.
[7] P. M. Dixon, J. Weiner, T. Mitchell-Olds, and R. Woodley,โBootstrapping the Gini coefficient of inequality,โ Ecology, vol.68, no. 5, pp. 1548โ1561, 1987.
[8] P. Bansal, S. Arora, and K. K. Mahajan, โTesting homogeneityof Gini indices against simple-ordered alternative,โ Communi-cations in Statistics: Simulation and Computation, vol. 40, no. 2,pp. 185โ198, 2011.
[9] E. I. Abdul-Sathar, E. S. Jeevanand, and K. R. M. Nair, โBayesestimation of Lorenz curve and Gini-index for classical Pareto
distribution in some real data situation,โ Journal of AppliedStatistical Science, vol. 17, no. 2, pp. 315โ329, 2009.
[10] S. K. Bhattacharya, A. Chaturvedi, and N. K. Singh, โBayesianestimation for the Pareto income distribution,โ StatisticalPapers, vol. 40, no. 3, pp. 247โ262, 1999.
[11] R. Kass and L.Wasserman, โThe selection of prior distributionsby formal rules,โ Journal of American Statistical Association, vol.91, no. 431, pp. 1343โ1370, 1996.
[12] J. Berger, โThe case for objective Bayesian analysis,โ BayesianAnalysis, vol. 1, no. 3, pp. 385โ402, 2006.
[13] J. Aitchison and I. R. Dunsmore, Statistical Prediction Analysis,Cambridge University Press, London, UK, 1975.
[14] J. O. Berger, Statistical Decision Theory Foundations, Conceptsand Methods, Springer, New York, NY, USA, 1980.
[15] R. V. Canfield, โA bayesian approach to reliability estimationusing a lossfunction,โ IEEE Transaction on Reliability, vol. R-19,no. 1, pp. 13โ16, 1970.
[16] H. R. Varian, โA bayesian approach to real estate assessment,โin Studies in Bayesian Econometrics and Statistics in Honor ofLeonard J. Savage, S. E. Fienberg and A. Zellner, Eds., pp. 195โ208, North-Holland, Amsterdam, The Netherlands, 1975.
[17] A. Zellner, โBayesian estimation and prediction using asymmet-ric loss functions,โ Journal of the American Statistical Associa-tion, vol. 81, no. 394, pp. 446โ451, 1986.
[18] R. Calabria and G. Pulcini, โAn engineering approach toBayes estimation for the Weibull distribution,โMicroelectronicsReliability, vol. 34, no. 5, pp. 789โ802, 1994.
[19] A. P. Basu and N. Ebrahimi, โBayesian approach to life testingand reliability estimation using asymmetric loss function,โJournal of Statistical Planning and Inference, vol. 29, no. 1-2, pp.21โ31, 1991.
[20] W. M. Afify, โOn estimation of the exponentiated Pareto distri-bution under different sample schemes,โ Applied MathematicalSciences, vol. 4, no. 8, pp. 393โ402, 2010.
[21] A. C. Cohen, โMaximum likelihood estimation in the Weibulldistribution based on complete and on censored samples,โTechnometrics, vol. 7, pp. 579โ588, 1965.
10 Advances in Statistics
[22] A. Ganguly, N. K. Singh, H. Choudhuri, and S. K. Bhattacharya,โBayesian estimation of the Gini index for the PID,โ Test, vol. 1,no. 1, pp. 93โ104, 1992.
[23] P. S. Laplace, Theorie Analytique des Probabilities, VeuveCourcier, Paris, France, 1812.
[24] H. Jeffreys, โAn invariant form for the prior probability inestimation problems,โ Proceedings of the Royal Society. London,Series A: Mathematical, Physical and Engineering Sciences, vol.186, pp. 453โ461, 1946.
[25] H. S. Al-Kutubi and N. A. Ibrahim, โBayes estimator for expo-nential distributionwith extension of Jeffery prior information,โMalaysian Journal of Mathematical Sciences, vol. 3, no. 2, pp.297โ313, 2009.
[26] H. Raiffa and R. Schlaifer, Applied Statistical Decision Theory,Division of Research, Graduate School of Business Administra-tion, Harvard University, 1961.
[27] I. S. Gradshteyn and I. M. Ryzhik, Tables of Integrals, Series andProducts, United States of America, 7th edition, 2007.
[28] S. K. Sinha and H. A. Howlader, โOn the sampling distributionsof Bayesian estimators of the Pareto Parameter with proper andimproper priors and associated goodness of fit,โ Tech. Rep. #103,Department of Statistics, University of Manitoba, Winnipeg,Canada, 1980.
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