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2 Hettmansperger–Randles Estimators of Location and Shape

# 2 Hettmansperger–Randles Estimators of Location and Shape

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192

Affine equivariance properties of the functionals imply that in the elliptic case,

.Fy / D and V.Fy / D p ˙ =Tr.˙ / D .

When location and shape functionals are applied to empirical distribution

function based on the sample Y D .y1 ; : : : ; yn /T , we obtain estimators that we

denote from now on by O D .Y/ and VO D V.Y/. The estimators are then naturally

affine equivariant as well and, in the elliptic model, all location and shape estimates

then estimate the same population quantities and and are directly comparable

without any modifications.

11.2.2 k-Step Location and Shape Estimators

The location estimator O based on a chosen location score function T.y/ solves the

estimating equation

avefT.yi

O /g D 0:

The corresponding location functional .Fy / is then defined by E T.y .Fy // D0.

If the identity score, T.y/ D y, were used, the classical sample mean vector is

obtained that is optimal in the case of multivariate normality. Optimal location

score function in the spherical case is T.y/ D r .jjyjj/. The spatial median,

Brown (1983), is obtained by using the spatial sign score function S.y/ defined

in (11.1). The spatial median is highly robust estimator of symmetry center having

50 % breakdown point and bounded influence function. It can be computed using a

simple iteration steps

Ok D Ok

1

C

avefS.yi O k 1 /g

:

avefjjyi O k 1 jj 1 g

The estimator is however only rotation equivariant, that is, it satisfies (11.3) only for

orthogonal p p matrices A.

The affine equivariant spatial median can be obtained using so called transformation-retransformation technique. In that case, the observations are first standardized, the spatial median is then found for the standardized observations, and

the estimate is then transformed back to the coordinate system of the original

observations. See Chakraborty et al. (1998), Tyler et al. (2009) and Ilmonen et al.

(2012) and the references therein. In Hettmansperger and Randles (2002), location

and shape estimators are estimated simultaneously: O and VO are chosen to satisfy

avefS.Oei /g D 0 and p avefS.Oei /S.Oei /T g D Ip ;

(11.4)

O D p. The resulting

where eO i D VO 1=2 .yi O / and VO is standardized so that Tr.V/

location estimate O is an affine equivariant spatial median and the shape matrix

estimate VO is the Tyler’s M-estimate, Tyler (1987), with respect to the spatial

median.

11 k-Step Hettmansperger–Randles Estimates

193

The statistical properties of HR estimators were studied in Hettmansperger and

Randles (2002), Tyler (1987), and Dümbgen and Tyler (2005). They showed

that the location and shape estimators have bounded influence functions, positive

breakdown points and limiting multivariate normal distributions. The computation

is very simple. As in general M-estimation case, the estimating equations (11.4) can

rewritten in a way that provides the following iteration steps.

Iteration Steps 1 The HR location-scatter estimate is obtained using the following

steps

1=2

1. eO i D VO k 1 .yi O k 1 /, i D 1; : : : ; n,

1=2

2. O k D O k 1 C VO k 1 ŒavefjjOei jj 1 g 1 avefS.Oei /g,

1=2

1=2

3. VO k D VO k 1 avefS.Oei /S.Oei /T g VO k 1 .

and VO k is standardized so that Tr.VO k / D p.

Unfortunately there is no proof for the convergence of the above algorithm nor

the existence and uniqueness of the HR estimates. It is however well known that

the convergence is attained if one repeats the steps 1 and 2 alone (spatial median)

or the steps 1 and 3 alone (Tyler’s scatter matrix). In the paper we proceed with the

same practical

p solution as in Taskinen et al. (2010), that is, we start the iteration

with some n-consistent estimates and stop iterating after k steps. The estimate

then inherits some properties of the initial estimate but, with large k, the behavior is

almost as that of the regular HR estimate. We then give the following.

Definition 11.1. Let O 0 and VO 0 be initial location and shape estimators. The k-step

HR estimators O k and VO k for location and shape are obtained by starting with O 0

and VO 0 and repeating Iteration Steps 1 k times.

Notice that the k-step estimators are affine equivariant if the initial estimators are

affine equivariant. In Croux et al. (2010), the robustness and efficiency properties

of k-step Tyler’s shape estimator were studied. They for example showed that the

breakdown property of the k-step estimator is inherited from the initial estimator.

The approach used in Croux et al. (2010) differs from ours in that the location center

is assumed to be fixed. In the following sections, we derive influence functions and

asymptotic properties for the simultaneous k-step HR location and shape estimators.

11.2.3 Influence Functions

The robustness of a functional T against a single outlier y can be measured using

the influence functions Hampel et al. (1986). Let

F D .1

/F C

y;

denote the contaminated distribution, where y is the cdf of a distribution with

probability mass one at point y. The influence function of T is then given by

194

IF.yI T; F/ D lim

T.F /

T.F/

!0

:

A continuous and bounded influence function indicates good local robustness

properties of an estimator. We now find the influence functions of the k-step HR

estimators in the elliptic case.

Due to affine equivariance properties of our estimators, it suffices to derive

influence functions at a spherical distribution F0 of e. Hampel et al. (1986) and

Ollila et al. (2004) showed that in that case, the influence functions of all location

and shape functionals, .F/ and V.F/, are of the form

IF.yI ; F0 / D .r/ u;

(11.5)

and

Ä

IF.yI V; F0 / D ˛.r/ uuT

1

Ip ;

p

(11.6)

where r D jjyjj, u D jjyjj 1 y and real-valued functions .r/ and ˛.r/ depend

both on the functionals and on the underlying distribution F0 . When comparing

robustness properties of different estimators, it is enough to compare weight

functions and ˛ only. In the following we will derive these functions for k-step

HR-estimators.

Let now k D k .Fy / and Vk D Vk .Fy / be the functionals corresponding to

k-step HR-estimators O k and VO k , that is,

1=2

k

D

k 1

C

Vk 1 EŒS.e/

EŒjjejj 1 

(11.7)

and

1=2

1

Vk D p ŒTr.Vk

where e D Vk

1=2

EF S.e/S.e/T Vk 1 /

1=2

1 .y

k 1 /.

1

1=2

1

Vk

1=2

EF S.e/S.e/T Vk 1 ;

(11.8)

We prove the following result in Appendix.

Theorem 11.1. The influence functions of k-step HR location and scatter functionals k and Vk with initial functionals 0 and V0 at F0 , the distribution of spherical

e with Cov.e/ D Ip , is given by (11.5) and by (11.6), respectively, with

"

Â Ãk

Â Ãk #

1

1

p Œ.p 1/E.jjejj 1 / 1 ;

k .r/ D

0 .r/ C 1

p

p

and

Â

˛k .r/ D

p

pC2

Ãk

"

˛0 .r/ C 1

Â

p

pC2

Ãk #

.p C 2/:

195

4

10

11 k-Step Hettmansperger–Randles Estimates

2

3

HR location.

3−step HR location

2−step HR location

1−step HR location

S−estimator

0

0

2

1

4

γμ

γμ

6

8

HR location

3−step HR location

2−step HR location

1−step HR location

mean vector

0

2

4

6

8

10

0

r

1

2

3

4

r

Fig. 11.1 Functions k for the k-step HR location functionals with k D 0; 1; 2; 3 and 1 when

the regular mean vector (left figure) and 50 % BP S-estimator with biweight loss-function (right

figure) are used as starting functionals. The functions are computed at the bivariate standard normal

distribution case

First note that, the influence functions of the regular HR location-scatter estimate is

obtained when k ! 1, that is,

.r/ D pŒ.p

1/E.jjejj 1 /

1

and ˛.r/ D p C 2:

The above influence functions are clearly bounded if those of the initial estimators

are bounded. In Fig. 11.1 we illustrate the behaviour of the function k at bivariate

standard normal case using two different initial estimators. When the sample

mean vector is used as a starting value, resulting k-step estimators have naturally

unbounded influence functions, although after few steps the influence function is

very close to that of the affine equivariant spatial median. When highly robust 50 %

breakdown point S-estimator with biweight loss-function, Davies (1987), is used

as an initial estimator, bounded influence functions are obtained. Notice that after

few steps the influence function does not differ much from that of the location HR

estimator.

In Fig. 11.2, the influence functions for k-step HR shape estimators are illustrated

at bivariate standard normal case. As initial estimators we use the sample covariance

matrix as well as the 50 % breakdown point S-estimator with biweight loss-function.

functions are obtained, but the estimator with better robustness properties is again

obtained after few steps. By using S-estimator as a starting value, the influence

functions of resulting k-step estimators are naturally bounded.

In the following section, we will compare the efficiency properties of k-step HR

estimators with different initial estimators. We will show that, after only few steps,

the initial estimator has very little influence on the resulting efficiencies.

10

HR shape

3−step HR shape

2−step HR shape

1−step HR shape

S−estimator

8

αV

30

0

0

2

10

4

20

αV

40

HR shape

3−step HR shape

2−step HR shape

1−step HR shape

covariance matrix

6

50

12

196

0

2

4

6

8

10

0

1

2

r

r

3

4

Fig. 11.2 Functions ˛k for the k-step HR shape functionals with k D 0; 1; 2; 3 and 1 when the

regular covariance matrix (left figure) and 50 % BP S-estimator with biweight loss-function (right

figure) are used as starting functionals. The functions are computed at the bivariate standard normal

distribution case

11.2.4 Limiting Distributions and Asymptotic Relative

Efficiencies

Thepasymptotic normality of k-step HR estimators follows if the initial estimators

are n-consistent and have limiting multinormal distributions. In the following, we

write vec.V/ for the vectorization of a matrix V, obtained by stacking the columns

of V on top of each other. We also denote

Cp;p .V/ D .Ip2 C Kp;p /.V ˝ V/

2

vec.V/vecT .V/;

p

where Kp;p is the commutation matrix, that is, a p2 p2 block matrix with .i; j/-block

being equal to a p p matrix that has 1 at entry .j; i/ and zero elsewhere.

Theorem 11.2. Let y1 ; : : : ; yn be a random p

sample from

p F0 , the distribution of

spherical e with Cov.e/ D Ip . Assume that n O 0 and n vec.VO 0 Ip / have a

joint limiting multivariate normal distribution. Then

p

d

n O k ! N.0;

1k Ip /

and

p

n vec.VO k

d

Ip / ! N.0;

where

1k

Functions

k

D

2

k .jjejj/

p

and

2k

and ˛k are given in Theorem 11.1.

D

EŒ˛k2 .jjejj/

p.p C 2/

2k

Cp;p .Ip //;

11 k-Step Hettmansperger–Randles Estimates

197

The limiting distributions at elliptical distribution follow from the affine equivariance properties of the estimators. See for example Ollila et al. (2004) and Taskinen

et al. (2010).

Corollary 11.1. Let y1 ; : : : ; yn be a random sample from F, an elliptical distribution of ˙ 1=2 e C where e is spherical with Cov.e/ D Ip . Write D .p=tr.˙ //˙ .

Then

p

n.Ok

where

1k

d

/ ! N.0;

and

2k

1k ˙ /

and

p

n vec.VO k

d

/ ! N.0;

are given in Theorem 11.2 and W D Ip2

p

2k

1

WCp;p . /WT /;

vec. /vec.Ip /T :

In order to compare asymptotic relative efficiencies of different estimators, one

only has to compare scalars 1k and 2k . In Table 11.1 we list the asymptotic relative

efficiencies of k-step HR location estimators as compared to the sample mean at

different p-variate t-distributions with selected values of dimension p and degrees

of freedom , where D 1 refers to the multinormal case. As in previous section,

we use the sample mean and 50 % BP S-estimator as starting values.

Table 11.1 Asymptotic relative efficiencies of k-step HR location

estimators as compared to the sample mean at different p-variate

t-distributions with selected values of dimension p and degrees of freedom

pD2

pD5

p D 10

k

1

2

3

4

5

1

1

2

3

4

5

1

1

2

3

4

5

1

(a)

D3

1.600

1.882

1.969

1.992

1.998

2.000

2.094

2.274

2.300

2.304

2.305

2.306

2.302

2.412

2.421

2.422

2.422

2.422

D6

1.135

1.135

1.115

1.101

1.093

1.084

1.238

1.250

1.250

1.250

1.250

1.250

1.297

1.312

1.313

1.313

1.313

1.313

D1

0.936

0.867

0.827

0.806

0.796

0.785

0.937

0.912

0.907

0.906

0.905

0.905

0.960

0.952

0.951

0.951

0.951

0.951

(b)

D3

2.025

2.045

2.031

2.017

2.009

2.000

2.359

2.318

2.308

2.306

2.306

2.306

2.451

2.426

2.423

2.423

2.422

2.422

D6

1.035

1.074

1.083

1.085

1.085

1.084

1.259

1.253

1.251

1.250

1.250

1.250

1.320

1.314

1.313

1.313

1.313

1.313

D1

0.697

0.747

0.768

0.777

0.781

0.785

0.898

0.904

0.905

0.905

0.905

0.905

0.950

0.951

0.951

0.951

0.951

0.951

The sample mean (a) and the 50 % BP S-estimator (b) are used as a

starting values

198

Consider first the efficiency results for the simple k-step estimator that uses

sample mean vector as a starting value. In case of high-dimensional data, the

k-step estimators are very efficient even in the multinormal case, and after few

steps the efficiencies are already very close to those of regular HR estimators. As

seen in previous section, in case of low-dimensional data, several steps are needed

to obtain estimator with reasonable robustness properties. When multinormal data

in considered, such estimator seems to lack efficiency. To study the effect of an

initial estimator to efficiencies, 50 % BP S-estimator was also used as a starting

value. When k is large enough, the initial estimator has very little influence on the

efficiencies. For example when different 5-step estimators are compared, regardless

of the distribution, the efficiencies are almost alike.

In Table 11.2 the asymptotic relative efficiencies of k-step HR shape estimators

are given as compared to the sample covariance matrix based shape estimator.

We again use the sample covariance matrix as well as the 50 % BP S-estimator

as starting values. As seen in Table 11.2, in multinormal case, the k-step shape

estimators are very inefficient no matter which estimator is used as a starting value.

For heavy-tailed distributions, the k-step estimators outperform the initial sample

covariance matrix. Again after five steps, the efficiencies are very close to those

of the limiting estimators, and the efficiencies of sample covariance matrix based

estimators are very similar to those of the S-estimator based estimators.

11.3 Hettmansperger–Randles Estimators of Regression

11.3.1 k-Step Regression Estimators

Assume next the linear regression model

yi D BT xi C ˙ 1=2 ei ; i D 1; : : : ; n;

where yi are the p-variate response vectors, B is the q p matrix of unknown

regression parameters and ˙ is the covariance matrix of the residuals. The q-vector

of explaining variables xi and the standardized p-variate residuals ei are independent

and ei is spherical around zero with Cov.ei / D Ip . Finally .xi ; ei /, i D 1; : : : ; n, are

iid. We may then also write

Y D XB C E˙ 1=2 ;

(11.9)

where Y D .y1 ; : : : ; yn /T and E D .e1 ; : : : ; en /T are n p matrices, and X D

.x1 ; : : : ; xn /T is an n q matrix.

The regression estimator BO based on the location score function T.y/ solves

avefT.yi

BT xi /xTi g D 0

(11.10)

11 k-Step Hettmansperger–Randles Estimates

199

Table 11.2 Asymptotic relative efficiencies of k-step HR shape estimators as compared to the sample covariance matrix based shape estimator

at different p-variate t-distributions with selected values of dimension p

and degrees of freedom

pD2

pD5

p D 10

k

1

2

3

4

5

1

1

2

3

4

5

1

1

2

3

4

5

1

(a)

D5

1.714

1.778

1.670

1.590

1.546

1.500

2.194

2.221

2.170

2.151

2.145

2.143

2.512

2.520

2.504

2.501

2.500

2.500

D8

1.091

0.941

0.846

0.796

0.774

0.750

1.205

1.119

1.086

1.075

1.073

1.071

1.301

1.261

1.252

1.250

1.250

1.250

D1

0.800

0.640

0.566

0.532

0.516

0.500

0.831

0.748

0.724

0.717

0.715

0.714

0.878

0.841

0.835

0.834

0.833

0.833

(b)

D5

1.377

1.472

1.495

1.500

1.500

1.500

2.173

2.159

2.149

2.144

2.143

2.143

2.521

2.505

2.501

2.500

2.500

2.500

D8

0.687

0.733

0.746

0.749

0.750

0.750

1.094

1.081

1.074

1.072

1.072

1.071

1.245

1.253

1.251

1.250

1.250

1.250

D1

0.458

0.489

0.496

0.498

0.499

0.500

0.744

0.723

0.717

0.715

0.715

0.714

0.851

0.836

0.834

0.834

0.833

0.833

The sample covariance matrix (a) and the 50 % BP S-estimator (b) are

used as starting values

With the identity score T.y/ D y, the classical least squares (LS) estimator for

O

model (11.9) is obtained. The solution BO D B.X;

Y/ D .XT X/ 1 XT Y is then fully

equivariant, that is, it satisfies

O

O

B.X;

XH C Y/ D B.X;

Y/ C H;

for all q

p matrices H (regression equivariance). Further,

O

O

B.X;

YW/ D B.X;

Y/W;

for all nonsingular p

p matrices W (Y-equivariance) and

O

O

B.XV;

Y/ D V 1 B.X;

Y/;

for all nonsingular q q matrices V (X-equivariance).

As in case of location estimation, robust regression estimator is obtained by

replacing identity scores used in (11.10) with spatial sign scores S.y/. This choice

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