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Multivariate Statistics and Probability
Multivariate Statistics and Probability: Essays in Memory of Paruchuri R. Krishnaiah is a collection of essays on multivariate statistics and probability in memory of Paruchuri R. Krishnaiah (1932-1987), who made significant contributions to the fields of multivariate statistical analysis and stochastic theory. The papers cover the main areas of multivariate statistical theory and its applications, as well as aspects of probability and stochastic analysis. Topics range from finite sampling and asymptotic results, including aspects of decision theory, Bayesian analysis, classical estimation, regression, and time-series problems. Comprised of 35 chapters, this book begins with a discussion on ...
Causal Models in Experimental Designs
This is a companion volume to the Causal Models in the Social Sciences, the majority of articles concern panel designs involving repeated measurements while a smaller cluster involves discussions of how experimental designs may be improved by more explicit attention to causal models. All of the papers are concerned with complications that may occur in actual research designs--as compared with idealized ones that often become the basis of textbook discussions of design issues. In thinking about the revision of that volume, considerable literature has accumulated. As a result, this volume attempts to bridge the gap in time and substance to that earlier effort. Blalock examined articles that se...
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Stochastic Processes and Functional Analysis
"Covers the areas of modern analysis and probability theory. Presents a collection of papers given at the Festschrift held in honor of the 65 birthday of M. M. Rao, whose prolific published research includes the well-received Marcel Dekker, Inc. books Theory of Orlicz Spaces and Conditional Measures and Applications. Features previously unpublished research articles by a host of internationally recognized scholars."
Experimental Design
Contributions to Iowa State University and Beyond; Design and analysis of experiments; Linear and non-linear models; Statistical and population genetics.
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