Statistics for Spatial Data.

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can solve even large-scale GPs extremely efficiently and reliably; at the same time a number of practical problems, particularly in circuit design, have been found to be equivalent to (or well approximated by) GPs. Putting these two together, we get effective solutions for the practical problems. The basic approach in GP modeling is to attempt to express a practical problem, such as an engineering analysis or design problem, in GP format. In the best case, this formulation is exact; when this is not possible, we settle for an approximate formulation. This tutorial paper collects together in one place the basic background material needed to do GP modeling. We start with the basic definitions and facts, and some methods used to transform problems into GP format. We show how to recognize functions and problems compatible with GP, and how to approximate functions or data in a form compatible with GP (when this is possible). We give some simple and representative examples, and also describe some common extensions of GP, along with methods for solving (or approximately solving) them.

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A tutorial on geometric programming

The Bootstrap and Edgeworth Expansion

A conditional Poisson model is proposed for situations where one wants to compare the rate parameters for several populations, adjusting for a 'size' parameter and the random e ects of the subjects. Sometimes the physical nature of the problem makes it logical to consider some order restrictions on the rate parameters. The rate parameters are estimated and tested for under such order restrictions. This investigation is motivated by a real-life example on butterfly behavior, and the estimation and tests of hypotheses are illustrated over this data set.

Statistical inference concerning weighted Poisson rates under some natural order restrictions

Likelihood ratio tests for and against decreasing in transposition in K×K×K contingency tables

We consider the competing risks problem with two risks and develop empirical likelihood ratio type tests for testing the null hypothesis that the cumulative incidence functions corresponding to these two risks are equal against the alternatives: (a) they are not equal and (b) they are linearly ordered. The asymptotic null distributions of the proposed test statistics are shown to have simple distribution-free representations in terms of a standard Brownian motion process. The results of a simulation study indicate that the proposed test for testing for the presence of the linear order is more powerful than a test designed for the same situation in Aly et al. (1994). To illustrate the theoretical results, we discuss an example involving survival times of mice exposed to radiation.

On Comparing Cumulative Incidence Functions Using an Empirical Likelihood Ratio Type Test

There is a substantial literature on testing for the equality of the cumulative incidence functions associated with one specific cause in a competing risks setting across several populations against specific or all alternatives. In this paper we propose an asymptotically distribution-free test when the alternative is that the incidence functions are linearly ordered, but not equal. The motivation stems from the fact that in many examples such a linear ordering seems reasonable intuitively and is borne out generally from empirical observations. These tests are more powerful when the ordering is justified. We also provide estimators of the incidence functions under this ordering constraint, derive their asymptotic properties for statistical inference purposes, and show improvements over the unrestricted estimators when the order restriction holds.

Hammou El Barmi

Papers

Statistical inference concerning weighted Poisson rates under some natural order restrictions

Likelihood ratio tests for and against decreasing in transposition in K×K×K contingency tables

On Comparing Cumulative Incidence Functions Using an Empirical Likelihood Ratio Type Test

Order restricted inference for comparing the cumulative incidence of a competing risk over several populations

On comparing competing risks using the ratio of their cumulative incidence functions