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

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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

Framework Convention on Climate Change

http://users.iems.northwestern.edu/~jiexu/research/compasscs.pdf

Speeding up COMPASS for high-dimensional discrete optimization via simulation

The 2012 NSF Workshop on Simulation Methodology took place in the School of Management, Fudan University, Shanghai, China from July 23, 2012 to July 25, 2012. The workshop was sponsored by the National Science Foundation (USA) under Grant Number CMMI-1219403, the School of Management of Fudan University, and the Financial Engineering and Financial Risk Management Branch of the Operational Research Society of China. The theme of the 2012 NSF Workshop was computer simulation: opportunities and challenges. With new technologies, such as parallel computing, graphic processing units (GPUs), and cloud computing, and emerging areas, such as health systems and environmental systems, there are opportunities for the simulation community to broaden and advance research and applications. Thus, there is a need for identifying new opportunities and challenges in research related to emerging technologies and application areas. More specifically, emerging computing technologies and application areas include parallel computing environments, GPU, cloud computing, financial engineering, healthcare and health system, and environmental system, etc. Under this theme, the goal of the meeting was to scope new opportunities and challenges in simulation research related to new emerging computing technologies and application areas, and brainstorm how to successfully approach them. In addition, compared to other areas of operations research, the simulation community is small in China and many other countries in East Asia. The workshop was to promote simulation research attracting more researchers to the field and build international collaborations. The workshop featured four 1-hour plenary talks, complemented by two 30-minute regular presentations followed by discussion sessions. The plenary talks presented were:

Guest Editors' Introduction to Special Issue on the 2012 NSF workshop

Revisit of stochastic mesh method for pricing American options

On one hand, large-scale ranking and selection (R&S) problems require a large amount of computation. On the other hand, parallel computing environments that provide a large capacity for computation...

Knockout-Tournament Procedures for Large-Scale Ranking and Selection in Parallel Computing Environments

Estimating portfolio risk measures and classifying portfolio risk levels in real time are important yet challenging tasks. In this paper, we propose to build a logistic regression model using data ...

/pdf/online-risk-monitoring-using-offline-simulation-15u2wwb2xx.pdf

L. Jeff Hong

Papers

Speeding up COMPASS for high-dimensional discrete optimization via simulation

Guest Editors' Introduction to Special Issue on the 2012 NSF workshop

Revisit of stochastic mesh method for pricing American options

Knockout-Tournament Procedures for Large-Scale Ranking and Selection in Parallel Computing Environments

Online risk monitoring using offline simulation