Kernel estimators of density function of directional data

Y

xcRk,p>O,

%w)=

{.Y: llv-XII

WY

xeRk,p>O.

We have the following lemma. LEMMA 3. If E(k) denotes the set of all open balls B(x, p) and B(k) denotes the set of all closed balls B(x, p), then B(k) and B(k) both belong to the V-C class with the same index s = k + 2 for all k = 1,2, ... .

For a proof see Wenoeur and Dudley [ 123 (1981). LEMMA 4 (A multinomial distribution inequality). Let n,, .... n, be the frequencies in m classes of a multinomial distribution in n = n, + . . . + n, independent trials. Then for all E E (0, 1) and all m such that (m/n) < ~~120, we have P l!I

(.

In,-En,/

>n.s

>

(1.14)

<3exp(-n&=/25).

For a proof, see Devroye [3].

2. POINTWISE STRONG CONSISTENCY We prove the following theorem on pointwise strong consistency of&x) as defined in (1.3) as an estimator of f(x). THEOREM 1. Let K(.) and (h,}

(a) (b)

O
(cl) (c2)

v(~~~)‘~K(v)=O lim,+, f is bounded on Q,

(d)

lim, _ o. h, = 0, and

(e)

lim,,

satisfy the following

conditions:

K is bounded on R + ,

m(nhf:-‘/log

or

n) = co.

Then at any continuity point x off,

lim f,(x) =f(x) n-m

a.s.

We need the following lemma to prove Theorem

(2.1)

1. For convenience of

KERNEL

ESTIMATORS

notation we write h for h, throughout theorems.

29

OF DENSITY

the paper except in the statements of

LEMMA 5. Suppose that the conditions (a) - (d) of Theorem 1 hold. Then at any continuity point x off

IEfJx) Further,

iff

-f(x)1

as

+ 0

n-too.

(2.2)

is continuous on Q, then

lim Sup IEfn(x) - f(x)1 = 0.

n-cc Proof

(2.3)

x

Using (1.5),


KC(l-x’y)P21

1 -x’.!J
+C(h)hl-k

s1 -

= I, + 12 + I,

x’y > 6

If(Y)--f(x)l

My)

KC1-Wlh21f(y)d~(y)

(say).

(2.4)

By continuity of f at x, we can find 6 >O for any given E>O such that If(y)-f(x)l<&for l-x’y<& Thus, by (1.5), Z,<&(h)h1-k/QK[1-x’y)/h2]do(y)=E.

Now, let condition

(c,) of Theorem sup K(u)u(~-~)‘~

Z,
D > 6/h=

(2.5)

1 hold. Then as n-rco

I ,f(y)dm(y)+O

(2.6)

by (1.2), (1.7), and conditions z = 2C(h) z(k-‘M2

2

zT(k-

1)/21

< (2~)‘~-

‘)“C(h)

’

fC(k-

1)/21

(cl) and (d) of Theorem K(u)[u(2

- h2u)](k-3)‘2

f(x)js6;zK(u)u(k-3)‘2du+0

1.

Further, we have

du

as n-*co

(2.7)

30

BAI,

RAO,

AND

ZHAO

by (1.4) and (1.7). Equations (2.5)-(2.7) imply (2.2). The results (2.2) when (c2) is true and (2.3) can be proved in a similar manner. Proof of Theorem 1. By Lemma 5, we have lim E&(x) =f(x); n - IX

(2.8)

also we shall prove that lim [f”(x) - Ef(x)] n-w so that (2.8) and (2.9) imply result. Put

= 0,

as.,

that f,(x) +f(x)

&=h’pkC(h)(K[(l

-x’Xi)/h2]

(2.9)

a.s., which is the desired

-EK[(l

-x’Xi)/h2]).

Then
O+%h)M

K2C(l -W/h21 f(y) d4y)

E(t;)4h2”-“C2(h)jn G Mh2” -“‘C’(h)

sR KCtl

-4W21

f(y)

(2.10)

ddy),

where M is an upper bound of K on R + . By (1.7) and Lemma 5, there exist constants a > 0 and u(x) > 0 such that Et;f
15, I 6dpk,

h’-k.

By Lemma 1,

PClfn(x) -f(x)l z El = p [n-l Ipi1 a] < 2 exp[ -nc2/(ach1-k = 2 exp[ -nhkBy condition

+ 2u(x) hlpk)]

‘E~/(~u(x) + a~)].

(e) of Theorem 1,

1 PClf,tx) - %.(x)l 2 ~1< 00=-f,(x) - Ef,(x) = 0 n i.e., (2.9) holds, which together Theorem 1.

with (2.8) implies

a.s.,

(2.1), the result of

ICERNELESTIMATORSOFDENSITY

31

3. UNIFORM STRONG CONSISTENCY In the following we assume that ~1is a measure on !J with f(x) as the pdf and ZL, is the empirical measure based on the sample A’,, .... X,. We have the following theorem which is parallel to that for the standard case given by Bertrand-Retali [4]: THEOREM 2. Suppose that f is continuous on D and K is bounded on R, and Riemann integrable on any finite interval in R + with

~0~S~p{K(~):~&~J<1}u~k~3”2du
If h,-tO

(3.1)

and (nhi- ‘/log n) + ~13

asn-,oO,

(3.2)

then

sup If,(x)-f(x)1 x

+O

a.s.

(3.3)

ProojY The proof of Theorem 2 is similar to that of Theorem 1 in Devroye and Wagner [S]. Here we give only a sketch of the proof. By Lemma 3 of Devroye and Wagner [S], for each q+,6 small and p large we can find a function K*(v) = f ail,,(v), where Z,, is the indicator 0) (ii) (iii) (iv) union has (VI

function:

a1 9ee.3aNo are non-negative numbers, A,, .... A,, are disjoint intervals contained in [0, p], [K*(v) - K(v)1
We note that continuity off on B implies that it is uniformly and f(x) < M, (some constant) on Q. By Lemma 5, sup Efnb) -f(x)1

+0

a.s.as

n+co.

continuous (3.4)

32

BAI,

RAO,

AND

ZHAO

Putting

wx)=~‘-kCo~

R I~C~~-~‘y~/~*l-~*C~~-x’y)/h2])f(y)dw(.Y),

~2n(x)=h’-kC(h)

JloK*c(1 -x’.YM21 4P,(.Y)-p(y)1

,

we have sup Ifn(x) - W”(X)I d i sup U,,(x). j=, x x Following

(3.5)

Devoye and Wagner [5], we can prove that SUP U,,(x) x

can be made arbitrarily Let Ai*(

and

sup U,,(x) x

(3.6)

small by choosing q, 6 small and p large enough. (yd2:

[(1 -x’y)/h*]EAi}.

Then

(say). Hereafter, c denotes a positive constant but may take different values at different appearances, even in the same expression. If we choose A, = [ai, bi), i= 1, .... NO, then A:(x)={L.E52:~h~lly-xll<~h}. Writing d=

{A,*(x):xcQ,

i= 1, .... N,}

we have by Lemma 3, m”(n) < 2(nk+2 + 1)2

for any n.

(3.8)

33

KERNEL ESTIMATORSOFDENSITy

Hence d is a V-C class with some index s as defined in the text following (1.9). Then using Lemma 2 quoted in Section 1, we have P{sup U,,(X)~E}


(3.9)

By (3.2), we have 1 P[sup U,,(x) BE] < co x n Then, by the Borel-Cantelli

for any

E> 0.

lemma, sup U,,(x) + 0

a.s.

(3.10)

(3.5), (3.6) and (3.10) complete the proof of Theorem 2.

4. STRONG L, -NORM CONSISTENCY

In the following, we establish under some conditions the strong L,-norm consistency of fn(x), i.e.,

IR If,(x) -f(x)1 d4x) + 0

0.

(4.1)

The precise statement is given in Theorem 3. THEOREM 3.

Suppose that (a)

fom u(~-~)‘*K(u)

lb)

h,+Oandnhf:-‘-+ooasn-+co.

Then, for any given

E

-f(x)1

do(x)

2 E} de-“.

By (l.S),

vn = I I%z(x) -f(x)1 R

O-(h)j

(4.2) (4.3)

> 0, there exists a constant c > 0 such that

P {IQ If,(x) Proof:

dv < GO,

n

ddx)

ddx)j- KC(1-x’y)lh211f(~)-f(x)ld~(y). R

(4.4)

34

BAI,

RAO,

AND

Given E> 0, we can find a non-negative such that

ZHAO

continuous

function

g(x) on Q (4.5)

I R If(x) - g(x)1 dMx) < 46. Then

Gj

R +

hl-kCw~(x)j

R

KC(l --wl~21 If(Y)-dY)l

I, A’ -kC(h) ddx) jQ a-(1 -x’.YPl

+ j~h”‘C(h)do(x)SnKC(l--x’y)lh21

If(x)-

d4.Y) &)I d4Y)

IdY)-g(x)1 d4.Y)

say.

=J,,+J,,+J3,,

(4.6)

By (1.5) and (4.5),

=5pIf(Y)-g(y)1 DDE)<+

(4.7)

In the same way, JZn < 46.

(4.8)

Let us denote M,=sup{g(x), XEQ} and Q,(x)= {~EQ: As in (3.9), we can take p sufficiently large such that il, hl-kC(h)

do(x) j

KC(l - w/~21 I ET(Y)- bG)l d4Y) QI(.Y)

bq$-kw)

j/4x)

EM h’-kC(h)(27t)‘k-‘)‘2 &! TCW- 1)/21 By uniform continuity jQhl”C(h)do(x)

1 -x’y>ph2}.

jQ;,~x)KIu-x’Jw21 d4.Y) jQ ddx) jpmdk

- 3”2K(u) dv < ~112.

(4.9)

of g(x) on Q and (1.5), we see that for large n, j

KC(l -.hW21 IVY)-&)I

R~ Q1C.x)

l~-~C(h)

K[(l

-x’y)/h2]

My) do(y)
KERNEL

By (4.6~(4.10),

ESTIMATORS

35

OF DENSITY

for large n, V”
(4.11)

Take K*(u) > 0 such that

C(h)(2z)‘kI)” ,zqu) _~a(~), du <$j7 z-E& “fk- 3)/2

(4.12)

lY23

and put (4.13) i=l

As in (1.6) we have sR IL(x) -fZ+(x)l

ddx)

r=l i R < C(h)(27#’

1w

T[(k-1)/21

lK[(l

-x’Xi)/h2]-K*[(l

I R [K(u)-K*(u)1

-x’X,)/h’]I

~‘~-~“~dt1<~/6,

dw(x)

(4.14)

and

s R

I%,(x) - WW)I ddx) < &/6.

(4.15)

We can take K*(v)=

f

~jZ,&),

j=l

where A,, .... A, are disjoint finite intervals on R,. By (4.11), (4.14), and (4.15), in order that (4.4) holds, it is enough ro prove that for any .sl >O, there exists a positive constant c such that P

If,*(x)

- Efn*(x)l

Here we can take K*(u) = ZCa,b)(u).

dw(x)

> e, < epc”.

(4.16)

36

BAI,

RAO,

AND

ZHAO

For x = (x1, . ... xk)’ E 0, we can represent x in polar coordinates x1 = cos

81

.x2 = sin 8, cos 8, .. xk-l=sintI,...

(4.17) sin8,p,c0s8,-,

x,=sintI,...sinfJ,~, with 0 G ei 6 7~, i = 1, .... k - 2 and 0 < ok- 1 6 27~. Such a representation unique except for a Lebesgue null set H c Q. Take L > 0, and put

is

J:) = Ix = x(e,, .... ek~,)~~-H:L~‘h(ij-i)~ej
ikp,=lr2 .I;) =

j = 1, ...) k - 2,

,..., v- 1= [h-‘2L7c],

{X = x(e,, .... e,_,)d2-H:

(IA-

i)L-%~ej67tj,

j = 1, .... k - 2,

{x=x(eI,

q-l)=

.. ..

ek-,)EQ-HH:(v-i)L-lh~ek~I~2K}

and k-Ji,

._. &,

=

1

n j=

Jy),

il ) . . . . ik-

2 =

1,

2, .... u; ik _ I = 1, 2, . ... u.

1

All these Ji, jkm, constitute a pertition Take c and L such that

Y of Q - H. and

c>max{,/%k3’2,,/%k3i2+(2L)-1k3}

2L-‘c
Put

A= [a,b),

B=[a+L-‘c,b-L-‘c]

A*(x)={~E~--H:a<(l-x’y)/h2
XESZ-H,

B*(x)={y~51-H:a+cL-‘<(l-x’y)/h2
u

J,

XEQ-H,

XEQ-H.

JE Y,JcA*(.r)

Now we proceed to prove that for x E Q - H, G(x) = A*(x)

- D(x) c A*(x)

- B*(x)

= G*(x).

(4.18)

KERNEL

ESTIMATORS

37

OF DENSITY

Assume that y = y(P,, .... f$- 1) E G(x). Then y#D(x), and there exists a set Ji,...4-, and a point w = w(tI;, .... I~~-~)EJ~~...~~-, such that but

WEJil...ikm,

YEJil...ik-17

o#A*(x).

Thus l@j-0,llI -CL-‘h, and by (4.17), jyj-ojl
But o#A*(x)*Ilx-wll implies that I/x-

(4.19)

where yj and oj (4.20)


or 11x-w/I
>fih

y/l > (,,b-k3’2Lp1)h

which in turn

or

J/x- y/l <(fi+k3’2L-1)h

or

l-x’y<(a+cL-‘)h*.

i.e., l-x’y>(b--CL-‘)h*

Thus YEA*(X)-B*(x), Since K*(u)=Z,(u),

(4.21)

and (4.18) is proved. we have

f R If,*(x) - W,*(x)l d(a) =hl-kC(h)

sR IPL,(A*(x))-PV*(X)I

d4x)

Gh’ - kW) JI, JE~ ,c A (-~)IL,(J) - 14J)l do(x) ,c * + h’-kW)

I R CAG*(x)) + ,QG*b-111 do(x)

(say).

= Zln + z2n For any probability

(4.22)

measure v on 0, we have 5R vCG*(x)l

h’-kC(h)

=I

dv(y)

f*

hl-kC(h)ZA_B[(l

~ C(h)(2n)ck-

rC(k-

da(x)

‘)‘*

lIPI

-x’y)/h2]

v(k-3’12 dv < ~,/3 sYEA--B

do(x)

(4.23)

38

BAI,

RAO,

AND

ZHAO

by taking L sufliciently large. Thus Z,, < 2&,/3.

If JE Y, y~JcA*(x),

XEQ-

<

(4.24)

H, then (1 -x’y)
I i-2Ico,~, [(I - x’yl/h2]

do(x)

Hence

< chk- ‘,

(4.25)

where c is a positive constant. Thus by (4.22) and (4.25), we have

Since #(Y) d ch’~ k = o(n) by (4.3), Lemma 4 can be used. Thus by (4.22), (4.24), and (4.26), we have

where c>O is a positive constant, which proves (4.16) the desired result.

REFERENCES [l]

BATSCHELET,E. (1971). Recent statistical methods for orientation. Amer. Inst. Biol. Sci. Bull.

[2] BATSCHELET,E. (1981). Circular Sfatistics in Biology. Academic Press, London. [3] DEVROYE,L. (1983). The equivalence of weak, strong and complete convergence in L, for kernel density estimates. Ann. Starist. 11 896904. [S] BERTRAN~FRETALI, R. (1978). Convergence uniforme dune estimateur de la densite par la method du Noyau, Rev. Roumaine Math. Pures Appl. 23 361-385. [S] DEVROYE,L. P., AND WAGNER, T. J. (1980). The strong uniform consistency of kernel density estimates. In J. Multivuriufe Anal. Vol. 5 (P. R. Krishnaiah, Pd.), pp. 59-77, North-Holland, Amsterdam. [6] HOEFFDING,W. (1963). Probability inequalities for sums of bounded random variables. J. Amer. Statist. Assoc. 58 13-30. [7] MARDIA, K. V. (1972). Statistics of Direcfional Data, Academic Press, New York.

KERNEL

[8]

PUKKILA, T., AND RAO, Discriminant Functions.

ESTIMATORS

OF DENSITY

39

C. R. (1986). Pattern Recognition Based on Scale Invariant Technical Report 86-09. Center for Multivariate Analysis,

University of Pittsburgh. [9] RAO, J. S. (1984). Nonparametric methods in directional data analysis. In Handbook of Statistics, Vol. 4, pp. 757-770. Elsevier, Amsterdam/New York. [lo] VAPNIK, V. N., AND CHERVONENKIS, A. YA. (1971). On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab. Appl. 16 264280 (English). [ 111 WATSON, G. S. (1983). Staristics on Spheres, Wiley, New York. Cl23 WENOCUR, R. S., AND DUDLEY, R. M. (1981). Some special Vapnik-Chervonenkis classes, Discrete Math. 33 313318. [13] ZHAO, L. C. (1985). An Inequality Concerning the Deviation between Theoretical and Empirical Distributions. Technical Report 85-30, Center for Multivariate Analysis, University of Pittsburgh.