UN MADISON MATHEMSTICS RESEARCH CENTER A P SUMS UNCLASSIFIE FER4 MONSRR2648 DAAG29-0C04 RDr C91 04

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1 7 AD-A A NOTE ON EQUALT IN ANDERSON'S THEOREM(UA WISCONSIN 1/1 UN MADISON MATHEMSTICS RESEARCH CENTER A P SUMS UNCLASSIFIE FER4 MONSRR2648 DAAG29-0C04 RDr C91 04 ANA FS 7

2 1 3 III IlI.25iiI. MICROCOPY RESOLUTION TEST CHART NATIONAL BUREAU OF STANDARDS -I963-A

3 0 RC Technical Summary Report #2648 Mathematics Research Center University of Wisconsin-Madison 610 Walnut Street Madison, Wisconsin February eceived January 16, 1984) C., LUJ C.2A p o v d f r u b l c T I C P-=Distrbuilon unlitned 0D E E D Sponsorcd by U. S. Army Research office p. 0. Box Research Triangle Park Noth Carolina

4 UNIVERSITY OF WISCONSIN-MADISON MATHEMATICS RESEARH CENTER A NOTE ON EQUALITY IN ANDERSON'S THEOREM *Andrew P. Some Technical Summary Report #2648 February 1984 ABSTRACT In this note it is shown, by a counterexample, that the necessary and sufficient condition for equality in Anderson's theorem, given by Anderson (1955), is incorrect. A more general condition is given which is shown to be necessary and sufficient. This is applied to the multivariate normal distribution. cation. Distribution/ ' Availability Codes Av ail and/or - ~& jet Special ANS (MOS) Subject Classifications: 60E15, 62H99 Key Words: Anderson's theorem, convexity, elliptically contoured distributions, multivariate normal distribution, symmetry about the origin, unimodality Work Unit Number 4 (Statistics and Probability) ii Sponsored by the United States Army under Contract No. DAAG29-80-C-0041 and the University of Wisconsin-Milwaukee...~.. -.

5 SIGNIFICANCE AND EXPLANATION Anderson's theorem is a widely used tool to obtain multivariate inequalities. Sometimes it is important to know when these inequalities are strict. This note gives a corrected necessary and sufficient condition for this to be the case. The responsibility for the wording and views expressed in this descriptive sumary lies with NRC, and not with the author of this report.

6 A NOTE ON EQUALITY IN ANDERSON'S THEOREM Andrew P. Some 1. INTRODUCTION Anderson's theorem (1955) is an important and widely used tool in probability and statistics. For an extensive discussion, applications and generalizations the reader is referred to Tong (1980, Chapter 4). The purpose of this note is to show, by a counterexample, that the necessary and sufficient condition for equality, given by Anderson (1955, p. 172) and quoted in Tong (1980, p. 54), is incorrect and to give a valid necessary and sufficient condition and to apply it to the multivariate normal distribution. 2. A COUNTEREXAMPLE AND NECESSARY AND SUFFICIENT CONDITIONS FOR EQUALITY We begin by establishing notation and stating assumptions. Let x = (X1,X2,...,xk) be a point in R, f(x) : R k + [0,-) be unimodal, symmetric about the origin and such that f f(x)d; < -, A C R k be convex, A symmetric about the origin and have a nonempty interior, Du = {xlf(x) u,, u > 0, A n D u have a nonempty interior for at least one u > 0, y * 0 an arbitrary vector in Rk, and X e (0,1]. Then Anderson's theorem (1955) states that with equality for all X e (0,I) if and only if for every u > 0. f f()dx f f(x)ax, (2.1) A+Xy A+y [(A + y) n Dul - [(A n Du)] + y (2.2) We first consider a counterexample. Let a > 0, k - 2, 22 -(x1 2)/2 A {(x 1,x 2 )1-a x I a), f(;) - (2w)- e, y - (0,1). Here the Sponsored by the United States Army under Contract No. DAAG29-80-C-0041 and the University of Wisconsin-Milwaukee.

7 Du'a are circles with center at the origin and clearly However, (A + y) r D - [A ) D u [A ) D u I +y. (2.3) f f(;)dx - f f(x)dx (2.4) A AA X e [0,1), contradicting Anderson's assertion that (2.2) is a necessary condition for (2.4). (2.4) also contradicts the assertion made by Tong (1980, p. 54) that if f is a multivariate normal density with positive definite covariance matrix E and mean vector 0, then the inequality in (2.1) is always strict for A C [0,1). While Tong attributes this statement to Anderson (1955), it does not seem to have been made by him. We now state and prove a more general necessary and sufficient condition for equality in (2.1). Theorem 2.1. A necessary and sufficient condition for equality in (2.1) (for all A such that A V [0,1)) is that, for each u > 0, there exists a vector R Rk, k which may depend upon u, such that [(A + y) n DU] - [A n DU] + z (2.5) Proof. From the proof of Anderson's (1955) theorem, necessary and sufficient conditions for equality are (obtained by setting X - 0 in (2.1)) 1 1 [A r) D u ] -- [(A + ) Dl u +- -D (( = ) ] 2 U 2 u, (2.6) and [(A+ ) DJ 2[(A - y) D)D + z, (2.7) where 2 is an arbitrary vector which may depend on u. (2.7) is, for this Minkowski inequality. It is readily verified that - (A y D I - [A r DuJ I 2(A + ;) n Du] + 2 [(A- ) D] (2.8) and so only the reverse inclusion must be show in order for (2.6) to hold. case, the necessary and sufficient condition for equality in the Brunn S_ - ---

8 For sufficiency we must show that (2.5) implies (2.6) and (2.7). From the hypotheses, -[(A + y) n Du] ((-A - y) n (-D] [(A - y) n D U U U ] (2.9) and from (2.5) -[(A + y) n D I -[(An D) + z] [(An D ] - z (2.10) and so (2.9) and (2.10) imply [(A -y) nl D I (A n D)] - z. (2.11) From (2.5) and (2.11), the reverse inclusion in (2.6) follows and so (2.6) holds. (2.5) and (2.11) also immediately imply that (2.7) holds, thus proving sufficiency. Consider now the necessity. We must show that (2.6) and (2.7) imply (2.5). From (2.7), [(A - y) n D I 2 (A + y) n D z (2.12) and substituting (2.12) in (2.6) gives 11 [A n DU] -1 t(a + ;) n D 3 + (A + -) n D u 2 u 2 ( u (2.13) - ((A + ;) n D I - z since [(A + Y) n D u ] is convex, and (2.13) is equivalent to (2.5), proving the necessity. Note that the covnterexample satisfies (2.5) with z 0. That z = 0 in the counterexample is not an accident. We state a result to that end and indicate the proof. Corollary 2.1. Let [A n D u ] + z - ((A + y) n Du]. If An Du - A, then A is bounded and z y. If A n D u m Du, then D u is bounded and z -0. If A + y A, then A n D u is bounded and z 0. If k, z0 or -3- I-

9 Proof. If A n - A, then A is bounded, since f f()dlx < and A is A convex. From the hypotheses, [A ) Du] + z - A + z - [(A + y) r) DU] (2.14) and thus A + zc A + y A+;-A+y and so and V(A + z) - V(A + y) and therefore zy, since A is bounded. If A r) D u - Du, then Du is bounded, since f f(x)dx < - and D u is A convex. From the hypotheses, [A n Dul + =u D + [(A +) n D u ] (2.15) and thus Du + z C Du and therefore z = 0, since D u is bounded. If A + A, then A n D u is bounded, since f f(g)dx < - and A A ) D u is convex. From the hypotheses, and therefore z = 0, since A D u is bounded. [A ndu 3 + z = [A n Du 3 (2.16) If k = 1, then either A n D u = A or A f Du - Du, giving the conclusion. Note that for k > 2, z can take values other than 0 and y. For example, let a > 0, k = 2, A = {(x,x 2 )1-2a < x( 2a), a- 1x 2 1, 0 4 I'x 2 1 a flxl,x 2 ) ( 2.17) 1 0 Ix 2 1 > a and y - (yl,y 2 ) be arbitrary. Then, for u 4 a, and D u {(xl,x 2 ) I 1x 2 1 < a - u) (2.18) [(A + ) D] [A n Dl u + (Y, 0 ). (2.19) A similar result holds if A is bounded and f defined over a rectangle, provided y is sufficiently small. Incidentally, the above is another counterexample to Anderson's necessary and sufficient condition. -4- '- " : T

10 Note also that there are other probability inequalities in Anderson (1955), based upon (2.1), and in them the conditions for equality must be changed in accordance with Theorem 2.1. A possible difficulty with Anderson's (1955) proof of the necessity and sufficiency is that it seems to assume that when (A + y) n Du] and [(A - y) n Dul are translates of each other, without loss of generality the translating vector may be taken as 2y. The counterexample shows this is not necessarily true. We now apply the above to the multivariate normal distribution. Theorem 2.2. Let A be bounded and f(x) be the multivariate normal density with positive definite covariance matrix E and mean vector 0. Then the inequality in (2.1) is strict for all X C [0,1). Proof. Without loss of generality we may assume that X - 0 and E = I, since convexity is preserved under nonsingular linear transformations. Thus *the Du's are circles with center at the origin. Since A is bounded, there Is a smallest u such that Du contains A, say u 0. Then (2.5) is violated for u 0, giving the conclusion. Corollary 2.2. If A is bounded and f(x) is elliptically contoured with nonincreasing g, i.e., f(x) I-1g(x x), E positive definite, then the inequality in (2.1) Is strict for all C [(0,1). Proof. g nonlncreasing implies f is unimodal (Tong, 1980, p. 74) and then the proof is exactly as above. :

11 REFERENCES Anderson, T. W. (1955). The integral of a symmetric unimodal function over a symmetric convex set and some probability inequalities. Proc. Amer. Math. Soc. 6, Tong, Y. L. (1980). Probability Inequalities in Multivariate Distributions. Academic Press, New York. APB/ad

12 SECURITY CLASSFPICATIO14 OF THjIS PAGE (Whom Date E~ee. REPORT DOCUMENTATION PAGE ERE CMPLET M I. RPOR NUBER2. GOVT ACCESSION NO S.RECIPIENT S CATALOG NUMBER 4. TTLE and ubtile)5. TYPE OF REPORT & PERIOD COVERED A NOTE ON EQUALITY IN ANDERSON' S THEOREM Summary Report - no specific reporting period S.PRFORMING ORG. REPORT NUMBER 7. AUTHOR(s) I. CONTRACT OR GRANT NUMBER(@.) Andrew P. Soms DAAG29-8o-C PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT. PROJECT, TASK Mathmatcs eseach entr, Uivesit ofarea a WORK UNIT NUMBERS Mathmatis CeterUnivrsit Reearc ofwork 610 Walnut Street Wisconsin Unit Number 4- Statistics and Madison, Wisconsin Probability I. CONTROLLING OFPICE NAMIE AND ADDRESS 12. REPORT DATE U. S. Army Research Office February 1984 P.O. Box is. NUMBER or PAGES LResearch Triangle Park, North Carolina I.MONITORING AGENCY KNME a ADDRIESS(If differet bein Cmtrol!laW Office) IS. SECURITY CLASS. (of this 'epov) IS& UNCLASSIFIED DECL ASSI FICATION/ DOWNGRADING SCHEDULE -16. DISTRIBUTION STATEMENT (of this Report!) Approved for public release; distribution unlimited. 17. DISTRIBUTION STATEMENT (of the abstract enterd In block 20. It different ban Report) IS. SUPPLEMENTARY NOTES It. KEY WORDS (Continue an rovere side It neeary ad identify fir block number) Anderson's theorem, convexity, elliptically contoured distributions, multivariate normal distribution, symmetry about the origin, unimodality 20. ABSTRACT (Condmiue on revrsee aide It ntesty. mad identify by block uasor) In this note it is shown, by a counterexanple, that the necessary and sufficient condition for equality in Anderson's theorem, given by Anderson (1955, is incorrect. A more general condition is given which is shown to be necessary and sufficient. distribution. This is applied to the multivariate normal D OA," 147n EDITION OF I NOV 5IS OBSOLETE UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (Wen Does EntersO