On the Optimal Rates of Convergence for Nonparametric

Deconvolution Problem

by

Jian(ling FanDepartment of StatisticsUniversity of CaliforniaBerkeley, CA 94720

Technical Report No. 157Junie 1988

Researchi suipportedResearch suppolted

by NSF Grant No. DMS84-51753by NSF Grant No. DMS87-01426

Department of StatisticsUniiversity of CaliforniaBerkeley, Californiia

ON THE OPTIMAL RATES OF CONVERGENCE FORNONPARAMETRIC DECONVOLUTION PROBLEM

Jianqing Fan

Departenteitt of Statistics

Un1iersity of Californiia, Berkeley

Berkeley, CA 94720

Abstract

Suppose we have n observations fromti Y = X + c, wlhere e is measurement error with

known distribution, and the density f of X is u"known with the non-parametric constraint

f E f (x): If( '(x) -f(')(x + 6)I <. B a, If I <CC. Suppose the functional of interest is

T(f) = f(')(xo) ; for I = t, T(f) is Xthe density function at a poinlt. Then the optimal rate of

m +aC-l

estimatinig T(f) is 0((log ii)1 ) if the tail of thle chtaracteristic function of e is of order

m +az-lI I Pbexp(- I t I P/y) as t - o and is 0 (11 2(m + a) + 21 + I) if thle tail of the characteristic func-

tioni is of order 0 (t- 0). Moreover, tlle optimal rate of convergence of a distribution function is

also found, wlich is no longer "root-n consistency" as in the ordiniary case. In addition, the

optimal rate of es(inmating the functional T(f) = aj f ()(xo) is also addressed.

KEY WVORDS: Deconvolution; nonparamietric deiisity estimation; Estimation of Distribution;

Optimyial rates of convergence; Miiiinasix risk; Kernel estimation; Fourier Transformation;

Smiioothnless of error.

Abblreviated title: Optim1al rates of Decoiivohltiion

- 2 -

1. Introduction

Suppose we have observations Y, *** Y, having the same distribution as that of Y

available to estimnate the unknown density f(x) of a random variable X, where

Y =X +£ (1.1)

with measurenient error £ of knownt distribution. Assunie furtherimiore that the random vari-

ables X and e are independent. We will discuss herein l1ow well the unknown density and its

distribution functioni can be estimated noniparametrically under certain smoothness conditions.

The usual smootimess condition imnposed onI f is the set with kth bounded derivatives.

More generally, we shall assume f satisfies Lipschhitz condition of order a, i.e. f is in the

set

nt,B = (f(x): If "').x) -f(m)(x + 0)1 5 BO¶ If I C) (1.2)

whiere B, C, and 0 . a < 1 are constants. Theni ithe optimal rate of convergence will depends

on mn, a through nt + a.

Sucih a model of measurements beinig cotitaminiatede with error exists in niany differenit

fields and has been widely studied. For examnple, the observation Y is the survival time of an

animal, X is (lie tiune that a tumor occurs, and e is the time from tumor occurring to death.

Some oth1er extniples, described in Liu and Taylor (1987), Carroll and lIall (1987), are Crunip

and Seinfeld (1982), Mendelsohn and Rice (1982), and Medgyessy (1977).

The applications for such a model in tiheoretical settings are mentioned in Carroll aned

Hall (1987). For example, our results call be applied to the estimation of the prior of non-

parametric empirical Bayes problemii (Berger, 1980). Also, the theory can be aplplied to non-

parametric regression estimationi for estimation of regression functiotn and to thie generalized

linlear model ( Stefinski anid Cairoll (1987) ), andt otier moodels suclh as Y = Xe.

Carroll and Hall (1987) give tlie optiniial rates of deensity estimation at a point whien tile

error is niornal, and give the result for gamnia distributions omitting thle proof. As far tlhe

author can deterinine from their proof, we don'lt not know exactly why the lower rates should

- 3 -

depend on the tail of te. In our heuristic argtiment in section 3, the reason for this dependence

are clearly stated. We will generalize the result to inore general setting by assuwling that thle

tail of clharacteristic function is eithler of fonn

I4e(t ) I =O (I t I Poexp(- I t I /y)) (as t - oo) (1.3)

or

+£t) = 0 (C P) (as t -o) (1.4)

Moreover, we will give the rate of convergence of estimating distribution , which is not n 1/2

conivergence any more.

We will address how the difficulty of deconvolition depen(ds heavily on both imposed

smootlhness conidition on density f and on the sniootlmness conidition of distribution of error.

By snioothness of tie distribution of error, we mean tlhe order of characteristic function te(t)of £ as t--*o. The difficulty can be explainied inttuitively: onl the basis of finiite observations,

the tail of t,(t) makes it difficult to identify tlhe tail of clharacteristic function of X, and hence

the distribution of X. It can also be explained by the fact that tlie empirical characteristic func-

tioii of Y does not go to 0 ( as t -- oo ), anid lhence to use inversion formula heuristically, we

lhave to truncate the integration somlewlhere. The smoother of the distribution of C, the more

we have to tnincate, whicih increases thie variance anid bias of the estimator. Tlhus, we have to

pay some extra cost for estimaning the density of f in deconvolution form. Therefore, the

optimal rate depends on how nmuch we pay for tlhe extra cost.

To find the optimial rate of cotivergenice, we usually liave to linid a lower bound and anl

upper bound. Suppose we wanlt to estiimate a functional T of f. In primiciple, we can apply lihe

niodulus lower bounid b(b ) invenite(d by Donolio and Liu (1987), and defined by

b(e) = supl IT(f) - T(f2) 1: f 1i f 2 E Cni,a,B, H (fY, fY2) . -I (1.5)

int cuirrent seltitig, where H (fy 1. fyX2) is thie Hellinger metric between fy I an(l fY2 (see Lecamil

(1973), (1985)) and( fy, is the denisity of Y unider the convolution of no(lel (1.1). But

- 4 -

constructing a lower bound in this way inay obscure the essential difficulty inside. Instead of

using Hellinger metric, we will use x2 metric. To fomiulate the idea, let

Xkic f 2) = j 1 - f2)2f- 1d (1.6)

be Ihe x2 metric between two densi(ies, andt

br(e) = SUP( IT(f1) - T(f2) 1: f 1. f 2 E Cm,a. XVY(If fY2) . e (1.7)

Then, the lower bound bT( lW ) will be the attainable lower bound, and the rate cani be com-

puted explicitly. In otlher words, when we cannot distinguish between two densities based on n

observations of Y. then the change of functional is a lower bound.

To find an achievable upper bounid, we will utse Parzen's (1962) type of denisity estima-

tor. A similar construction is used by Stefanski and Carroll (1987) and Liu and Taylor (1987).

For a nice kernel function K(x), let K(t) be its Foturier transfonn with tK(O) = 1. Then the

kernel density esliimator is defined by

00 A

f"x)= -! J exp(- itxr) (th). dt (1.8)

for suitable choice of bandwidth h and kerniel function. Note (1.8) can also be written in ker-

nel type of density (see (2.3)). Moreover, we will use (1.8) to construct tlie estiinators of the

derivatives of thie unknown density and its distribution.

In section 2, we will exhibit the rates of kernel type of density estimators , which are the

optnmal in terms of rates of convergence. In sectioni 3, we will constrtict the lower bounds and

give hetuistic argument of the results which allow us to say the optimal rate of convergence.

hi section 4, we will give somie brief (liscussioni anid coimimlents. bI section 5, we will give the

proofs of the results.

- 5 -

2. Kernel density estimators

Let's start with the kernel density estimator (1.8) with empirical characteristic function

delinied by

A~~~n

n(=- exp(itY.) (2.1)

and a given known funjction tK (t ). Let K(x) be the Fourier inversion of K(t ) defined by

+ 00

K(x) =2n

exer(- i tK+(t)dt, (2.2)

a smooth kernel function. Then (1.8) can be rewritten as a kernel type of estinmator:

fnT.,)=- E -

, ( ) (2.3)

wlhere

+ 00

gh,rx)=- I exp(-itKv) dt (2.4)2ir t4(tlh)(24

Define the maxinuin mean square error (MMSE) of an estimator f to be

MMSE(f)= sip E(f (rx)-f (x0))2 (2.5)f 6 Cm,a

To compute (2.5), first conmpute its bias and see wthat kind of kernel or equivalently its

Fourier transformationi 4K(r), we should use.

+ 00

Efn (xt) - f(xf) = J exp(- itx(,) 4K (tIh )x (t) dt - f (x(.)

h00

Tlle last expressioni does not dependl ont thte error (listribution. Thius, tlie niiniilutim conditions

we hlave to hipose are that the Kernel function K satisfies the conditions of those without

- 6 -

convolution. We will state them oIn its Fourier domain.

-The conditions we are going to impose on K amd on 4, are

Al) *jt) * 0, for any t.

A2) *K (t ) is a symmetric function, havinig IU + 2 bounded integrable derivatives on

( , + oo).

A3) *K(t)= 1 + O(ItIa'm ), as t -- 0.

Note that A2) and A3) is inmposed simply to make kemel K(.) satisfy the condition of

"classical" (witlhout convolution) kernel function. Additional contditions will be specified

below.

More generally, we can use f,AI (x0) to estimate f(')(x0), the 1th derivative of the unk-IIowiI density at xo. For thle exponenitial decay of te case (we will call this the supersmootd

case), we have thle followinig rates of convergence.

Thteorem 1: Under the assumlptions Al)- A3) and

El) *K(t)=O for ItI .1.

E2) Ite(t)1 It I "'exp( I t I1P/y) > c (is t -+ oo) withi 3, y, c > 0.

I- 'I

Tlhen by clhoosing tlhe bandwidthih = (4/y) P (log i ) P,

f:9 E (f,,'o(x)) *lf('x))2 = 0 ((logn )- 2(m +

a- 1)13) (2.7)

f eimal

Remark 1: When I = 0, f,, (x(p) is the estimator of density functioni itself, which has rate

of convergence of 0 ((log n ) (m + Wx)/0). The constant I in condition El) is not essential. It

can be replaced hy any positive conistanlt. Tlhe reason we impose such a condition is simply to

make (1.8) converge, and for easy calculation in the proof.

For the case of geometric decay of tE (called thle smiioothness case), we have the follow-

il)g result.

- 7 -

Tlheorem 2: Under the assunmptions Al) - A3) and

GI) 4)+'(t)t1 + -PC, te(t)tp -+ c, (t - +0) wi(h c > O.

+00 +00

Then by choosiiig the bandwidth h = 0 (n - 1/[2(m + a) + 21 + 1),

2(m + a - l)

SLIP E(f,n,l')(txf)f()(x())2 = 0 (ii 2(m + a) + 21 + (2.8)f e Cm al

Remark 2: If we want to estimate T(f) = z ajf()(xo) in Cm,a,B, then thle kernel den-

sity estimator T(f,) = ajf,"(j)(x) (a, * 0) has the optimlal rate of 0 ((log n ) (m + of - 1)11) or

m +a-1

o (fl 2(m + + P + I), depending on the rate of the tail of 4e. The proof of such a result fol-

lows easily from the proofs given in this awid the next section uider the same assunptions.

Note that the rate of convergence given by (2.7) an(d (2.8) is the optimal one, which will

be shown in Ile next section.

Now, we conisider an estimator of distribution function. Define thle estiniator of distribu-

tioni F (@r() by

xo

ft, (,0o) = ff(t) dt (2.9)

where f, (t) is the kernel denisity estimnator given by (2.3).

Theoreim 3: Under assumptionis El), E2), Al) of Theorem 1, stippose th)at K(t) is a

symmetric function, having m+3 bound(led initegrable derivatives on ( - 0, +° ), and

4K (t) = I + 0 ( It I + + a), as t -* 0. Tlhemi by choosinig the same bandwidth as for Theoremn

I and M,, = 13 we lhave:

sup Ef (A, (-x(t) - F(.))2 = 0 ((log n 2(m + a + 1)/P)f E c m.aB

- 8 -

whiere C'm,," - If r Cm,0,: FF(-n) e D(log n)-(m +2yP.

Remark 3: In the proofs of Theorem 5 & 7, we will see that the rate given in Theorem

3 is the optimal one, because the least favorable pair we choose is in C'm,vi,".

3. Lower bounds

In this section, we will lined lower bounds for estimating densities and distributions.

More generally, suppose we want to estimate T(f) from observation (1.1). Then we have the

following lower bound of bT( c )

Theorem 4: If for some sequence of positive constants (an: n 2 1 1, we have

limif igf Pf IIf - T(f)I . a, I=1n eo t sm,uj

tlien

lim,inf a, lbTf- 2 1/2 (3.1)if -*00 Nl;

Moreover for any estihmtortf of T(f), we liave for V c,

sIP Ef (f - T (f ))2 > Cb 2 (3.2)

for sonie constants C. In other words, no estimator can estimiate better that bT( c )

Remark 4: (3.2) is implied by the result of Donolho and Liu (1987) in thle current set-

(itig. In fact, such a theorem may be familiar to somne authors. For the purpose of later use

we state here anid give a proof.

Now, let's study the lower botund of b1. (-) To begin with, suppose ilte futnctionial of

interest is T(f ) = f (x(,), density at a poinit. Let's give a heuristic argunient to see why the

result shiould depened on the tail of te. Rigorous proof will be given in section 5, wlhich

inivolves mathiematical details and nmore careful conistructions.

-9-

Let's assume without loss of generality that xo = 0 by relocating xo to origin. To calcu-

late the abstract bouInd bT (f ), take a pair f0(x ) e Cm ,aB, f I(x ) 6 Cm ,aB, for which

f l(x) = f (x) + c68H(x,8) (3.3)

+ 00

whiere k = nt + a, H (0) * 0, H(x) dy = 0 and the m"' derivative of H(x) satisfies

Lipschitiz condition of order a. Then by suiitable clhoice of the tail of H(x), fO(x) and con-

stant c, f 1 will be a density in Cm ,a,B for small 6. 6 is chosen such that X2-distance

+00

J (fyI -fY2)2ff I'dX S C (3.4)n

and the lower bound of density estimnation at a point will be hialf of the change of functional

IT(fo) - T(f,)1/2 = 2 IH(0)16=0(68k) (3.5)2

Iliius, we have to lind 8 as larger as l)ossible so that (3.4) holds, or equivalently such that

+00 + 00

62k + I jY. 1- 8) r< IH(x _-)dF£(6)))2 g0 (6x)dx .O(-) (3.6)_ 00 _00

where Fe is the distribution functiont of the randoni variable £, go = fO*FC.

Suppose we can prove that as 6 -4 0,

+ 00 + 00

J [ J H (x -y )dF (6y )] g (ex ) dx (3.7)+00 +00

+ 00 + 00

.C J [ J H(x - y)dF(6v)] dx_ 00 _0

wlhere C is a constant independient of ni. Then by Parseval's identity, to make (3.5) hold, we

havc to chtoose 6 fromn

+ 00 + C'

62k +12 =H(x -y )dF (6y)2 dc =O(-) (3.8)J00 00

- 10-

or equivalently from

+ 00

62k+ J| I*CH(t)#(t/8) 1 2dt IO (3.9)00 ~~~~n

where tH is die Fourier transformation of H. Tlius, the result will depend on the tail of £

only. It is not hard to choose 5 front (3.9) anid consequently to get a desired lower bound.

Theorem 5: Suppose thtat the tail of , satisfies

I4)e(t)l It I POexp(t IP/y) . c (as t o)

aid P ix + Ix l' 2:x - IxI = O(IxI (a °))for 0OSo<I (as x -±oo) for

0 . ab < 1, a >0.5, then no estimaittor cani estinate T(f) = f,(1)(xO) knowing f e Cm,,,, faster

than 0 ((log n (, + a - P) in the sense tliat if

limilmf inif Pf If(')(xo) - f ('xo) I . a. I = 1 (3.10)n of e B

thlen

(log n )(m +a-a)/ ,an -c00 (3.11)

Moreover, for any estimator tP,I

sup Ef (T,T - T(f ))2> 0 ((log n ' +a-l)/P) (3.12)f Citm ,",

From dte result given in Theorenm 1, we klow that the optimal rate of estimtiatilig a den-

sity in the supersniooth noise case is only of order 0 ((logn )- (m + a)/P)). Specifically, wheii tile

error is distributed as Cauchy, thieni t(ie optimial rate is 0 ((logn - (t + a)), and witeci tlie error is

normal, then the optinmal rate is 0 (logn +ot)/2(

Th1eorem 6: Suppose that the tail of +£ satisfies tlte conidition GI) of Theorein 2, and

¢"(t )t(a + 2) -* a(a + 1 )c (t -+ oo), then no estimnator can estimate T(f) = f (1(xu) , under

m +a-I

tlte constraint thiat f e C,a,, faster than 0(11 2n# + 2a + 20 +I) in the sense of (3.1) anid

- i1 -

(3.2).

Remark 5: In some cases, *(t) = exp(ite0)4(t), where +(t) satisfies the condition of

Theorem 6. Then fe itself doesn't satisfy the conditions of Theorem 6, but by translation the

result still holds. Note that the coiistant c can be 0 in Theorem 6.

Remark 6: For estimating the functional T(f) in Remark 2, the lower bounds are

exactly the same as those given in Theoreni 5 and 6 using the same constructions.

Thus, we get the optimal rates for the smootlh cases and the supersnmooth cases. In prac-

tice, those con(itions are easy to check. The cases of error distributions satisfying Theorem 2

& 6 iniclude ganmina distribution, double exponenitial distribution , etc. Anid the cases of error

distributions satisfyinig Tlheoremn 1 & 5 are tiormiial, cauclhy, niixture normal, and niany othler

distributions. Now, we state some lower bounds for estimating the distribution function.

Theorem 7: Under the condition of Thleorem 5, then no estimator of estimation the distri-

m +cz+ I

bu(ioni function of X at a pohit ui(ler conts(rainit (1.2) can be faster than 0 ((logn) )

in the sense of (3.10) - (3.12).

Tlteoreni 8: Under the condition of Theorem 6, then no esihnator of estimation the dis-

tribution function of X at a poillt un(der constraint (1.2) caui be faster than

m +aOt I

0 (n 2m + 2a + 21 + I ) when B . 0.5 and 0 (i 112) when , < 0.5.

Remark 7: For estimating PF ( X E (a ,b J ), the lower bounds are the same as those in

Theorem 7 & 8. However, the lower bouni given by Theorem 8 may not be attainable. The

m +a+ I

attainable one iight be o (n 2(m + + P +1)).

- 12 -

4. Discussion

We hope to decompose the difficulty of deconvolutioi into two parts: the difficulty of

deconvolving a functional T, and modulus function without conlvolution (does not depend on

the tail of characteristic function). The filrst part tells us how difficult of deconvolution is for a

functional, and the second part tells us the difficulty of estimating a functional even though no

convolution exists. To formula the idea, let modtulus function

+ 00

f f I IT(f 1) -T(f 2)1: (f I -f2)2fFXI.)

be the difficulty function of estimating T(f) without convolution ( i.e. the lower bound if error

£ = 0 ). And one way to define the difficulty of deconvolving a functional is

+ 00 + 00

DT(O) =fIf22(Y'S ( J Y I -fY2)fy)f'dx: J (fXf)2jdx 6)

whlere C = ((f 1, f2):f I f 2 CCtaB, IT(f) - T(f2) b()/2)an)d then the lower bound

for estimating T(f) is b (Dj ( )). The lower boundi suggests that we find a pair of density

functionIs whichi is the least favorable in thle situation without convolution and such that the X2

distance is as small as possible. Of course, we hope to find a difficulty functioin D, which

does not depenid on T. But, it is imnpossible simply looking at estimating the me.l and density

of the normal error case e - N(0, 1). In this case, D (6) = 0(6) for estimating t(le mean, while

2(m + a +1P) + ID (6) = 0 (6 2(nm + a) + I) for estimating tlie (lensity.

Wihen error e is unifornily dlistributed on [0,lJ, say, the model (1.1) itself is ideentifiable.

Theorem 6 tells us that no estimator can estimate the density of X at a poinlt faster than

ni +a0 (it 2(no + a +1) + I ) for aniy b < 1. Hlowever, we canmot use kernel density estimsator (1.8) to

estimiate the density, because (1.8) is iiot initegrable alhiost surely.

- 13 -

5. Proofs

Proof of Theorem 1

According to our remtiark in section 2, the function K(t) satisfies thie conditionis of a kernel

futnctioni in density estimation in the situationl of no convolution. Tius, we can apply thle result

of classical kernel density estimationx (2.6) (see Rao (1983), P 46 - 47), and it follows that

sup} IEf,(l'I(x0) - f(I)(k() If E

+ 00

t SwB f( )(.,o - Y)t K( / ) dy -f(')(xO) I

S Chk-I

for some constanit C, where k = ni + a. Now thie varianice of f,,{'kxo) is

+ 00

var(f/)(x02))=22 I | (-it)' exp(-=it(1o[- Y (h dt

+ 00

1 E IJ it)l exp(- it(x.0 - Y1)) (fK dt 12(2i)i E (- 40e(V)

+lI t 'I K(t) 1 2

(2it)2u1a2 l4r(t/)l-dt] (5.1)

By assumption E2, when Mhi 5 I t I . 1 (for large but fixed M),

I e(t l/i ) I > C (t/h ) exp(- hF PIy)2

Moreover, by (Al)

I Ihe(rh)I >. mini 4e(t) > 0, w1hen I t I < Mh

ThIus, by (5. 1)

var ( v()(rO)) < 0 (exp(2hi -(2n2ih9ii2

- 14 -

- I=o(n 3)

by cioosing the bandwidth h = 0 ((4/y) p (log n) P). Hence, the conclusion follows.

Lemma 5.1: Under the assumiption of Theorem 2,

hg2[gh(\(.)j2 D2 (uifornly in small h).x2

for some constant D.

Proof of Lemma 5.1:

By integration by parts,

+ 00

r 4~~~~~K(t)g(l)(<x) = ± j exp(- itr)[(- it), ] dtix ~Jt/h)

Thus, by the usual argument,

+0

if, 2'[gj(I)(.X)j2 2 ( hP 1[ t ] I dt)X2 - 41e(tlh/+ 00

5 21 tKI¢(t)tP+'-'l + ICK01) t +lwt,2

(uniformly in sinall h ) for some constant C. Hlence, thie assertioni follows.

Proof of Theorein 2:

By choosinig the bandwidth as giveni by Theoremii 2 and by the calculation of Tlieorem 1,

we haive

k - I

Sup IEI '(v00) -f(')(x()I .O(/hkl)=O(n 2k+2af+1)f tm.ai.8

whlere k = ni + a. Now, we ineed onily to comipute the variance of the estimnator. Let

ghl = g1(1). Then by (2.3),

- 15 -

var(f,)'xo)) h 221 Eg 2( h )nh~~~(5.2)

Let fy(y) be tlhe density of Y = X + e, (hen

+ 00

Eg2( 0 I ) = g28(Y )fyZ(xo-y) dyh h_o

(5.3)

Note that Ify(x ) I . C for all f e Cm a, Hence, the following result follows uwformly in

f 6 C,,,f,B. For any small rq,+ 00 + 00

I J fy(x0y) lg2(.Y)dy- fy(xO) JO g(y)dyI_ 00

h00

+ 00

= I |_(fy(x_-y)- f(0)) g3() dy I-00_1hoo

.5 max Ify(.v() - y ) - fy(.T) I B23Y ) dyH.v I yg-l II I sVIn h

+ j'2 1.. 2 ( ) dy + fy (x,)yh ghh |O ghl,(2h) dy

IyI: 1 h h

minax IfYy(XO-Y)-fY(xo) I + 12 + I3

It is easy to chieck by delinition that I g(')(y ) I . Ch- 2p unformly for small h and some con-

stant C. By Leinma 5.1,

1, = | l 2(_i) dyIyI .1hll

. Oghl(') dv +IV I X,,,.

J g,h(y) dyIV I

< Dh- 20 1_ dv + 2Ch -21.w I2

2 -

(5.5)

(5.4)

- 16 -

Applying Lemma 5.1 again, we have

2 f (o -y) y 2(Y dyI2=

y h g"hl'h1 'lg 2Yg3)

< 1 sl_lyDt_-2Ti IyI21t1/hy

- o(1- 2) (5.6)

Sinmilar reason shows

13 Ify(x0) I |O &2g(, )dy =o(h 21) (5.7)1 v .- 11/h h

Note thiat (5.4) and (5.5) implies that

+ 0

2g,(y) dy = O(1h-21) (5.8)_ 00

Combining (5.5) - (5.8), we conclutde fronm (5.3) that

2 I E gl,/2( )(-

2(k - I )I

0(h - 2P - 21 - 1)= 0(1t 2k + 2p + In

Henice, we get the desired conclusion.

Proof of Thteoremn 3

Xt +00

EP,,(x(o) = f(u -y)-L K(2)dy dii

+00

= (F(x0u-y) -F(-ni y )K(x)dJ- hi h

- 17 -

Now by the stanldard proof, we cait show that

su EFn (x0) - F (x0)If Cm,

+ 00 +00

. I f F(xo-y)i!KK(Y-)dy -F(x0)l + J F(-n"3-hy)IK(y)ldy

1/M3- pgl

:5Clim +a1+ 1 + O(F(- n-l"312)) + |IK(y)l dy

mI + a + I

=o((log) p )

On the other hand, the variance of F, (x.0) is

+ 00

var(F (x0))5( /3 +x) 2 2 d| lK(t)l/l )lt](27r)2nit-2 h

. 0 ("i 1'3 2 exp(2 h- /yI))

The coniclusio follows.

Proof of Theorem 4

Take a pairff, f 2 e CC,,a, stuch that it satisfies (3.4) and

bT(C) < IT(f ,)-T2)1 + o(a,) (5.9)

Then

Ef n [.fY2(Y)1) fY2yn )Y

2f I fy i(y) fy. (Y)

+ 00

=(1+ J (fYf-Y2)2/fy dIx)" .e (5.10)

wherefy 1 is the convolution of f, witll the (listribution of e. Henice by the Cauchy-Schwartz

inequality,

- 18 -

2

[Pf2 (IPi - T4f2)I . aj ] . ec Pf1 1,4 - T(f2)I . an) (5.11)

On the other hand, by (5.11) we liave

p,e I ITn - T(f2)I .k2a,)

2 Pf,IIT(f 1) - 4n 1 :5 an, ITV2) fn I < an)

=Pflz IT(f2)-Tn I :! an ) + o(1)

2 e- c (as ii -+ oo)

Hence, we conclude that

I T(f)-T(f 2)1 . 2an

and thie conclusion follows.

We need tlie following Leiniia in order to prove tlheorem 5 - 8.

Lemma 5.2: Suppose that F is a distributioni function, then the convolution density

+00

g0(x)= j Crgo(x) |212,. dF+()

satisfies

go(x) 2 D Ix 1- 2r

as x -+ 00 willh D > 0.

Proof: Choose M large enough such that

F(A)- F(-M)> 0

Then when lxl is large,

AfCr

g(() JI + - y)2), dFXy) I2r

- 19 -

Lemma 5.3: Suppose P x + Ix ° 2x - Ix I O = O(lx I (a %)) for

0 . oO < 1 and H(x) is bounded with H(x) = o(lx I m) (as x ± oo ). Then there exists a

large M and a constait C such that when I 6x 1 2 M,

+ c

J H(x -y)dFF(y) < C(6Ix I)-.5-(a -0.5)/2

if nzo(a -0.5) > 1.5 + (a-0.5)/2.

Proof: Divide the real line into two parts:

I I:I Y618 < IX 1Ia, 12 = Y: IX: Y/891 > IX I")

Theni, by simple algebra,

+ 00

J H(Yr-y)dFI(6y)

< + HH(x - y /8) dF,(y)

0(61X 01) 01

°) + o(lX I-ta

Now clioosing a = (a - 0.5)/2, thie coniclusion follows.

Proof of Thteorem 5:

By relocating x( to the origin, without loss of generality assume that x(p= 0. Denote

k = iti + a. Take a real function funiction H(.) satisfying the following contditions:

1. H(9)(O) * 0.

2. H(k)(x) is bounded continuous for each k.

3. H (r) = 0 (x - f), as x -- oo, for soime given mi11.

+ 00

4. JH()dx =0.

- 20 -

0

5. J H(xdx *0.

6. *l(t) = 0, when It I is outside [1, 2], where tH is the Fourier transfonnation of H.

To see why such a function H(.) exists, let's take a nonnegative syinmetric function 4(t)

whiichi vanishes outside [1, 21 whent t . 0 aii(d has conttiniuous first m(p bounlded derivatives (

m(7 is large enough such that Lemma 5.3 holds). Moreover, 4(t) satisfies

h\)(0) . h0(11) (5.12)

aid

'!sin t +(t ) dt ;& 0

where h(x) is the Fourier iniversion of t(t) defined by

2

h1x) = if[cos (tx) (t)dt (5.13)

Such a 4(.) exists because all func(itis satisfying thie above conditions are infinite dimiensionial.

Let H(x) = h (x) - ht (x + 1), then its Fourier transformation 4H(t) = (I - e- i)4(t), and H(x)

satisfies the condlitions 1 - 6.

Now take a pair of densities

Cr 2 r'ad f= f +cSk H(0/8)

Thlen, by Lemnma 5.2, f I is a denisity whien 8 is small, anid by clioosing r close to 0.5 and c

close to 0, fr, and f1 EC,a,B.

Denote go = fo*FC. Now the x2 (listance between the two denisities in conivolution space

is of order (c.f. (3.6))

+00 + 00

82kt J ( J H(x - y)dFW(y))g2o (Sx) dx_ 00 _ 0

- 21 -

j + 00 + 00

.62k+ J ( J H(x -y)dFe(8y))2dJr_ 00 _00

x / |~ (J|H(x -y)dFe(5y)/g (&t))2 dx (5.14)_ 00 _00

Note that by Parseval's identity the first terni of (5.14) is

+00

2

=2 IOH(t) 12 1 o(tI8) 12 dt

. 0 (56 2poexp(- 2- P/y))

uniiformly in small &. Let the minimiiuim value of go(0x) over [-M, M] be mg, which is bigger

than 0. By Lemmna 5.2 and 5.3, the second tern is bounded by

+00 +"

t- J [ H((x -y))dFe(6y)J2dx + . -(a - 0.5)/2 dX ( )-00 __ ~~~~~~~~16x1'>M D(8xYj22 =

Consequiently, when 5 - 0,

+ 00

(fI - fY2)2(fyI)-' dx < C6cexp(- - P/y) (5.15)_00

for soine constanits c and C. Taking

S = (log it + (C + l)log(log 1i)) r

(5.15). C =1I log i 1

amid thle change of tlie functional is

Iff (0)) - f (l) (0) I = O (8(k - 1)

- 22 -

Thus,

bT(!) -0 ((log n)f(k - 1))

and the conclusion follows.

Proof of Theorein 6:

Use the same notation as in the proof of Theorem 5. Take the same 0(t) except only the

first two continuous derivatives are reqtuired in this case. Now take a pair of denisities

Cr and f I = f0 + c8aH(xl6)

Let H (t) = (1 - e- i )t(t) be the Fourier transformation of H(rx), and define

O6(t) = (OH(t) O£(r/6))""

and

O,l(t) = litn 8- P O8(t)8 -H (

wlhichi converges uniformly hi It I e (1, 2J. Now, by Fourier inversion formula,

(5.16)

+ 00

J H(x - y)dF e(8y)_ 00

+ 00

1 J exp(- itr )Oj(t)te(t/8) dt_00

1 -| e IO(t) dtIs I, 1 2

LetN=I S 11l 2

I&-+ (t) - t(t)Idt, wliichi goes toO ( as 8 -+ 0 ). Then by (5.17),

+ 00

I 8- J H(x-y)dFe(&1') Sx2IN+ ___1-00~ ~ -x2 2ytx2I.I2

I o(t) I dt

Now, we are reazdy to compute (3.6). By Parseval's indentity, when 8 is small,

(5.17)

(5.18)

- 23 -

+ 00

I, ^ H(xIx I _ 0

+ 00 + 00

c ( H(x

-y )dFE(8y)) g4 I (6x) dx

- y)dFc£(8y )) dx

+ go

SC | It,j(t)+r((/8)12 dt_ oo

(5.19)

= Q (82 P)

wlhere g =fo*Fe does not vanish, and henlce C is a finite constant. By Lemma 5.2, and

(5.18)

+ 00

22-^6p H( (x -y )Fe8y))2

_ '(fx) dx2~~~| 2go

o

N 1 5I2 (t) I dt ]g (6x ) dxlx I. X

+I 5

= 0(1) (5.20)

Consequenitly, the X7-distaice in tihe Y variable ( see (3.6) ) is

;2(m + a) + I (I, + 82 PI2)= 0(n -)

and the change of the functional is

m +a-l

I T(f1) - T(f0) I = &m + a- 1 1t (/)(1) - ht()(0) I = 0 (i 2(m + a+)+ I)

Hience the conclusion follows.

Proof of Thteoreim 7 & 8: By ranslation, witlhout loss of geiierality assulne tlhat x0 = 0.

Take ille same least favorable pairs as use(d in Theoreim 5 and 6. Then the chanige of func-

tional is

- 24 -

0

IF1(O)-FO(O)I =6m+a I J H(xl6)dxl

= O(6' + a+ 1)

Hence the result follows.

ACKNOWLEDGEMENTS

The author gratefully acknowledges Prof. Peter J. Bickel for proposing the problem and

fruitful suggestions and discussions. The author would like to express his sincere thanks to

Toinas Billings for careful reading of the manuscript and useful suggestions.

- 25 -

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59. ALDOUS, D. and DIACONIS, P. (February 1986). Strong Uniform Times and Finite Random Walks.

60. ALDOUS, D. (March 1986). On the Markov Chain simulation Method for Uniforn Combatorial Distributions andSimulated Annealing.

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84. BRILLINGER, D. (December 1986). Some statistical methods for random process data from seismology andneurophysiology.

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95. CANCELLED

96. BRILLINGER, D.R. (June 1987). Some examples of the statistical analysis of seismological data. To appear inProceedings, Centennial Anniversary Symposium, Seismographic Stations, University of California, Berkeley.

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98. O'SULLIVAN, F. and WONG, T. (June 1987). Determining a function diffusion coefficient in the heat equation.99. O'SULLIVAN, F. (June 1987). Constrained non-linear regularization with application to some system identification

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101. DONOHO, D.L. and LIU, R.C. (July 1987). Pathologies of some minimum distance estimators. Annals ofStatistics, June, 1988.

102. BRILLINGER, D.R., DOWNING, K.H. and GLAESER, R.M. (July 1987). Some statistical aspects of low-doseelectron imaging of crystals.

103. LE CAM, L. (August 1987). Harald Cramer and sums of independent random variables.

104. DONOHO, A.W., DONOHO, D.L. and GASKO, M. (August 1987). Macspin: Dynamic graphics on a desktopcomputer. IEEE Computer Graphics and applications, June, 1988.

105. DONOHO, D.L. and LIU, R.C. (August 1987). On minimax estimation of linear functionals.

106. DABROWSKA, D.M. (August 1987). Kaplan-Meier estimate on the plane: weak convergence, LIL and the bootstrap.

107. CHENG, C-S. (Aug 1987, revised Oct 1988). Some orthogonal main-effect plans for asymmetrical factorials.

108. CHENG, C-S. and JACROUX, M. (August 1987). On the construction of trend-free run orders of two-level factorialdesigns.

109. KLASS, M.J. (August 1987). Maximizing E max S',/ES': A prophet inequality for sums of I.I.D. mean zero variates.

110. DONOHO, D.L. and LIU, R.C. (August 1987). The "automatic" robustness of minimum distance functionals.Annals of Statistics, June, 1988.

111. BICKEL, PJ. and GHOSH, J.K. (August 1987, revised June 1988). A decomposition for the likelihood ratio statisticand the Bartlett correction - a Bayesian argument.

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112. BURDZY, K., PFrMAN, J.W. and YOR, M. (September 1987). Some asymptotic laws for crossings and excursions.

113. ADHIKARI, A. and PfITMAN, J. (September 1987). The shortest planar arc of width 1.

114. RITOV, Y. (September 1987). Estimation in a linear regression model with censored data.

115. BICKEL, PJ. and RlTOV, Y. (Sept. 1987, revised Aug 1988). Large sample theory of estimation in biased samplingregression models L.

116. RlTOV, Y. and BICKEL, P.J. (Sept.1987, revised Aug. 1988). Achieving information bounds in non andsemiparametric models.

117. RITOV, Y. (October 1987). On the convergence of a maximal correlation algorithm with alternating projections.

118. ALDOUS, D.J. (October 1987). Meeting times for independent Markov chains.

119. HESSE, C.H. (October 1987). An asymptotic expansion for the mean of the passage-time distribution of integratedBrownian Motion.

120. DONOHO, D. and LIU, R. (Oct. 1987, revised Mar. 1988, Oct. 1988). Geometrizing rates of convergence, H.

121. BRILLINGER, D.R. (October 1987). Estimating the chances of large earthquakes by radiocarbon dating and statisticalmodelling. To appear in Statistics a Guide to the Unknown.

122. ALDOUS, D., FLANNERY, B. and PALACIOS, J.L. (November 1987). Two applications of um processes: The fiingeanalysis of search trees and the simulation of quasi-stationay distributions of Markov chains.

123. DONOHO, D.L., MACGIBBON, B. and LIU, R.C. (Nov.1987, revised July 1988). Minimax risk for hyperrectangles.

124. ALDOUS, D. (November 1987). Stopping times and tightness I.

125. HESSE, C.H. (November 1987). The present state of a stochastic model for sedimentation.

126. DALANG, R.C. (December 1987, revised June 1988). Optimal stopping of two-parameter processes onnonstandard probability spaces.

127. Same as No. 133.

128. DONOHO, D. and GASKO, M. (December 1987). Multivariate generalizations of the median and trmmed mean EI.

129. SMITH, D.L. (Decenber 1987). Exponential bounds in Vapnik-tervonenkis classes of index 1.

130. STONE, C.J. (Nov.1987, revised Sept. 1988). Uniform error bounds involving logspline models.

131. Same as No. 140

132. HESSE, C.H. (December 1987). A Bahadur - Type representation for empirical quantiles of a large class of stationary,possibly infinite - variance, linear processes

133. DONOHO, D.L. and GASKO, M. (December 1987). Multivariate generaIlizations of the median and trimmed mean, I.

134. DUBINS, L.E. and SCHWARZ, G. (December 1987). A sharp inequality for martingales and stopping-times.

135. FREEDMAN, DA. and NAVIDI, W. (December 1987). On the risk of lung cancer for ex-smokers.

136. LE CAM, L. (January 1988). On some stochastic models of the effects of radiation on cell survival.

137. DIACONIS, P. and FREEDMAN, DA. (April 1988). On the uniform consistency of Bayes estimates for multinomialprobabilities.

137a. DONOHO, D.L. and LIU, R.C. (1987). Geometizing rates of convergence, I.

138. DONOHO, D.L. and LIU, R.C. (January 1988). Geometrizing rates of convergence, m.

139. BERAN, R. (January 1988). Refining simultaneous confidence sets.

140. HESSE, C.H. (December 1987). Numerical and statistical aspects of neural networks.

141. BRILLINGER, D.R. (January 1988). Two reports on trend analysis: a) An Elementary Trend Analysis of Rio NegroLevels at Manaus, 1903-1985 b) Consistent Detection of a Monotonic Trend Superposed on a Stationary Time Series

142. DONOHO, D.L. (Jan. 1985, revised Jan. 1988). One-sided inference about functionals of a density.

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143. DALANG, R.C. (Feb. 1988, revised Nov. 1988). Randomization in the two-armed bandit problem.144. DABROWSKA, D.M., DOKSUM, KA. and SONG, J.K. (February 1988). Graphical comparisons of cumulative hazards

for two populations.145. ALDOUS, D.J. (February 1988). Lower bounds for covering times for reversible Markov Chains and random walks on

graphs.146. BICKEL, PJ. and RITOV, Y. (Feb.1988, revised August 1988). Estimating integrated squared density derivatives.

147. STARK, P3. (March 1988). Strict bounds and applications.

148. DONOHO, D.L. and STARK, P.B. (March 1988). Rearrangements and smoothing.

149. NOLAN, D. (March 1988). Asymptotics for a multivariate location estimator.

150. SEILLIER, F. (March 1988). Sequential probability forecasts and the probability integral transform.

151. NOLAN, D. (March 1988). Limit theorems for a random convex set.

152. DIACONIS, P. and FREEDMAN, DA. (April 1988). On a theorem of Kuchler and Lauritzen.

153. DIACONIS, P. and FREEDMAN, DA. (April 1988). On the problem of types.

154. DOKSUM, KA. (May 1988). On the correspondence between models in binary regression analysis and survival analysis.

155. LEHMAN, E.L. (May 1988). Jerzy Neyman, 1894-1981.

156. ALDOUS, D.J. (May 1988). Stein's method in a two-dimensional coverage problem.

157. FAN, J. (June 1988). On the optimal rates of convergence for nonparametic deconvolution problem.

158. DABROWSKA, D. (June 1988). Signed-rank tests for censored matched pairs.

159. BERAN, R.J. and MILLAR, P.W. (June 1988). Multivariate symmetry models.

160. BERAN, R.J. and MILLAR, P.W. (June 1988). Tests of fit for logistic models.

161. BRELMAN, L. and PETERS, S. (June 1988). Comparing automatic bivariate smoothers (A public service enterprise).

162. FAN, J. (June 1988). Optimal global rates of convergence for nonparametric deconvolution problem.

163. DIACONIS, P. and FREEDMAN, DA. (June 1988). A singular measure which is locally unifonn. (Revised byTech Report No. 180).

164. BICKEL, P.J. and KRIEGER, A.M. (July 1988). Confidence bands for a distibution function using the bootstrap.

165. HESSE, C.H. (July 1988). New methods in the analysis of economic time series I.

166. FAN, J[ANQING (July 1988). Nonparametric estimation of quadratic functionals in Gaussian white noise.

167. BREIMAN, L., STONE, C.J. and KOOPERBERG, C. (August 1988). Confidence bounds for extreme quantiles.

168. LE CAM, L. (August 1988). Maximum likelihood an introduction.

169. BREIMAN, L. (August 1988). Submodel selection and evaluation in regression-The conditional caseand little bootstrap.-

170. LE CAM, L. (September 1988). On the Prokhorov distance between the empirical process and the associated Gaussianbridge.

171. STONE, C.J. (September 1988). Large-sample inference for logspline models.

172. ADLER, R.J. and EPSTEIN, R. (September 1988). Intersection local times for infinite systems of planar brownianmotions and for the brownian density process.

173. MILLAR, P.W. (October 1988). Optimal estimation in the non-parametric multiplicative intensity model.

174. YOR, M. (October 1988). Interwinings of Bessel processes.

175. ROJO, J. (October 1988). On the concept of tail-heaviness.

176. ABRAHAMS, D.M. and RIZZARDI, F. (September 1988). BLSS - The Berkeley interactive statistical system:An overview.

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177. MILLAR, P.W. (October 1988). Gamma-funnels in the domain of a probability, with statistical implications.

178. DONOHO, D.L. and LIU, R.C. (October 1988). Hardest one-dimensional subfamilies.

179. DONOHO, D.L. and STARK, P.B. (October 1988). Recovery of sparse signals from data missing low frequencies.

180. FREEDMAN, DA. and PITMAN, JA. (Nov. 1988). A measure which is singular and uniformly locally uniform.(Revision of Tech Report No. 163).

181. DOKSUM, KA. and HOYLAND, ARNLJOT (Nov. 1988). A model for step-stress accelerated life testing experimentsbased on Wiener processes and the inverse Gaussian distribution.

182. DALANG, R.C., MORTON, A. and WILLINGER, W. (November 1988). Equivalent martingale measures andno-arbitrage in stochastic securities market models.

183. BERAN, R. (November 1988). Calibrating prediction regions.

184. BARLOW, M.T., PiTMAN, J. and YOR, M. (Dec. 1988). On Walsh's Brownian Motions.

185. DALANG, R.C. and WALSH, J.B. (Dec. 1988). Almost-equivalence of the germ-field Markov property and the sharpMarkov property of the Brownian sheet.

186. HESSE, C.H. (Dec. 1988). Level-Crossing of integrated Ornstein-Uhlenbeck processes

187. P1TMAN, J.W. (Dec. 1988). Neveu's branching process in Brownian excursions.

188. P1TMAN, J.W. (Dec. 1988). The equilibrium law of Brownian extrema.

189. BARLOW, M.T., PITMAN, J.W. and Yor, M. (Jan. 1989). Some extensions of the arcsine law.

190. STARK, PB. (Dec. 1988). Duality and discretization in linear inverse problems.191. LEHMANN, E.L. and SCHOLZ, F.W. (Jan. 1989). Ancillarity.

Copies of these Reports plus the most recent additions to the Technical Report series are available from the StatisticsDepartnent technical typist in room 379 Evans Hall or may be requested by mail from:

Department of StatisticsUniversity of CalifomiaBerkeley, Califomia 94720

Cost:* $1 per copy.