THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Statistics for Spatial Data.

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Convex Analysisの二,三の進展について

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

New point and interval estimators for quantiles that employ a control variate are introduced. The properties of these estimators do not depend on the usual assumption of joint normality between the random variable of interest and the control. Illustrative examples for queueing and stochastic activity network models are given. In those examples, the new estimators are superior to the standard estimator in terms of the mean squared error of the point estimator and the length of the confidence interval.

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Control variates for quantile estimation

“Input uncertainty” refers to the (often unmeasured) variability in simulation-based performance estimators that is a consequence of driving the simulation with input models (e.g., fully specified univariate distributions of i.i.d. inputs) that are based on real-world data. In 2012 Ankenman and Nelson presented a quick-and-easy diagnostic experiment to assess the overall effect of input uncertainty on simulation output. When their method reveals that input uncertainty is substantial, then the natural next questions are which input distributions contribute the most to input uncertainty, and from which input distributions would it be most beneficial to collect more data? They proposed a possibly lengthy sequence of additional diagnostic experiments to answer these questions. In this paper we provide a method that obtains an estimator of the overall variance due to input uncertainty, the relative contribution to this variance of each input distribution, and a measure of the sensitivity of overall uncertainty...

Quickly Assessing Contributions to Input Uncertainty

We develop and evaluate a two-level simulation procedure that produces a confidence interval for expected shortfall. The outer level of simulation generates financial scenarios, whereas the inner level estimates expected loss conditional on each scenario. Our procedure uses the statistical theory of empirical likelihood to construct a confidence interval. It also uses tools from the ranking-and-selection literature to make the simulation efficient.

A Confidence Interval Procedure for Expected Shortfall Risk Measurement via Two-Level Simulation

This paper provides an advanced tutorial on the construction of ranking-and-selection procedures for selecting the best simulated system. We emphasize procedures that provide a guaranteed probability of correct selection, and the key theoretical results that are used to derive them.

Barry L. Nelson

Papers

Control variates for quantile estimation

Quickly Assessing Contributions to Input Uncertainty

A Confidence Interval Procedure for Expected Shortfall Risk Measurement via Two-Level Simulation

Selecting the best system: Theory and methods

On control variate estimators