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Probability Matching Priors: Higher Order Asymptotics
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Contemporary Multivariate Analysis and Design of Experiments
This book furthers new and exciting developments in experimental designs, multivariate analysis, biostatistics, model selection and related subjects. It features articles contributed by many prominent and active figures in their fields. These articles cover a wide array of important issues in modern statistical theory, methods and their applications. Distinctive features of the collections of articles are their coherence and advance in knowledge discoveries.
A Modern Theory of Factorial Design
The last twenty years have witnessed a significant growth of interest in optimal factorial designs, under possible model uncertainty, via the minimum aberration and related criteria. This book gives, for the first time in book form, a comprehensive and up-to-date account of this modern theory. Many major classes of designs are covered in the book. While maintaining a high level of mathematical rigor, it also provides extensive design tables for research and practical purposes. Apart from being useful to researchers and practitioners, the book can form the core of a graduate level course in experimental design.
Fractional Factorial Plans
A one-stop reference to fractional factorials and relatedorthogonal arrays. Presenting one of the most dynamic areas of statistical research,this book offers a systematic, rigorous, and up-to-date treatmentof fractional factorial designs and related combinatorialmathematics. Leading statisticians Aloke Dey and Rahul Mukerjeeconsolidate vast amounts of material from the professionalliterature--expertly weaving fractional replication, orthogonalarrays, and optimality aspects. They develop the basic theory offractional factorials using the calculus of factorial arrangements,thereby providing a unified approach to the study of fractionalfactorial plans. An indispensable guide for statisticians inresearch and industry as well as for graduate students, FractionalFactorial Plans features: * Construction procedures of symmetric and asymmetric orthogonalarrays. * Many up-to-date research results on nonexistence. * A chapter on optimal fractional factorials not based onorthogonal arrays. * Trend-free plans, minimum aberration plans, and search andsupersaturated designs. * Numerous examples and extensive references.
Probability and Statistical Inference
Priced very competitively compared with other textbooks at this level! This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. Beginning wi
Estimation in Conditionally Heteroscedastic Time Series Models
In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.
Computational Methods in Statistics and Econometrics
Reflecting current technological capacities and analytical trends, Computational Methods in Statistics and Econometrics showcases Monte Carlo and nonparametric statistical methods for models, simulations, analyses, and interpretations of statistical and econometric data. The author explores applications of Monte Carlo methods in Bayesian estimation, state space modeling, and bias correction of ordinary least squares in autoregressive models. The book offers straightforward explanations of mathematical concepts, hundreds of figures and tables, and a range of empirical examples. A CD-ROM packaged with the book contains all of the source codes used in the text.
Survey Sampling
Since publication of the first edition in 1992, the field of survey sampling has grown considerably. This new edition of Survey Sampling: Theory and Methods has been updated to include the latest research and the newest methods. The authors have undertaken the daunting task of surveying the sampling literature of the past decade to provide an outst
Confidence Intervals on Variance Components
Summarizes information scattered in the technical literature on a subject too new to be included in most textbooks, but which is of interest to statisticians, and those who use statistics in science and education, at an advanced undergraduate or higher level. Overviews recent research on constructin
Applied Analysis of Variance in Behavioral Science
A reference devoted to the discussion of analysis of variance (ANOVA) techniques. It presents ANOVA as a research design, a collection of statistical models, an analysis model, and an arithmetic summary of data. Discussion focuses primarily on univariate data, but multivariate generalizations are to
