This book is written in the hope that it will serve as a companion volume to my first monograph. The first monograph was largely devoted to the probabilistic aspects of the inverse Gaussian law and therefore ignored the statistical issues and related data analyses. Ever since the appearance of the book by Chhikara and Folks, a considerable number of publications in both theory and applications of the inverse Gaussian law have emerged thereby justifying the need for a comprehensive treatment of the issues involved. This book is divided into two sections and fills up the gap updating the...
This book is written in the hope that it will serve as a companion volume to my first monograph. The first monograph was largely devoted to the probab...
"Ninety percent of inspiration is perspiration. " 31] The Wiener approach to nonlinear stochastic systems 146] permits the representation of single-valued systems with memory for which a small per turbation of the input produces a small perturbation of the output. The Wiener functional series representation contains many transfer functions to describe entirely the input-output connections. Although, theoretically, these representations are elegant, in practice it is not feasible to estimate all the finite-order transfer functions (or the kernels) from a finite sam ple. One of the most...
"Ninety percent of inspiration is perspiration. " 31] The Wiener approach to nonlinear stochastic systems 146] permits the representation of single-...
This volume is a collection of eight Case Studies in Data Analysis that appeared in various issues of the Canadian Journal of Statistics (OS) over a twelve- year period from 1982 to 1993. One follow-up article to Case Study No.4 is also included in the volume. The OS's Section on Case Studies in Data Analysis was initiated by a former editor who wanted to increase the analytical content of the journal. We were asked to become Section Co-Editors and to develop a format for the case studies. Each case study presents analyses of a real data set by two or more analysts or teams of analysts...
This volume is a collection of eight Case Studies in Data Analysis that appeared in various issues of the Canadian Journal of Statistics (OS) over a t...
About 10 years ago I began studying evaluations of distributions of or der statistics from samples with general dependence structure. Analyzing in 78] deterministic inequalities for arbitrary linear combinations of order statistics expressed in terms of sample moments, I observed that we obtain the optimal bounds once we replace the vectors of original coefficients of the linear combinations by the respective Euclidean norm projections onto the convex cone of vectors with nondecreasing coordinates. I further veri fied that various optimal evaluations of order and record statistics, derived...
About 10 years ago I began studying evaluations of distributions of or der statistics from samples with general dependence structure. Analyzing in 78...
Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness...
Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior an...
The aim of this book is to give the reader a detailed introduction to the different approaches to generating multiply imputed synthetic datasets. It describes all approaches that have been developed so far, provides a brief history of synthetic datasets, and gives useful hints on how to deal with real data problems like nonresponse, skip patterns, or logical constraints.
Each chapter is dedicated to one approach, first describing the general concept followed by a detailed application to a real dataset providing useful guidelines on how to implement the theory in practice.
The...
The aim of this book is to give the reader a detailed introduction to the different approaches to generating multiply imputed synthetic datasets. I...
In order to precisely model real-life systems or man-made devices, both nonlinear and dynamic properties need to be taken into account. The generic, black-box model based on Volterra and Wiener series is capable of representing fairly complicated nonlinear and dynamic interactions, however, the resulting identification algorithms are impractical, mainly due to their computational complexity. One of the alternatives offering fast identification algorithms is the block-oriented approach, in which systems of relatively simple structures are considered. The book provides nonparametric...
In order to precisely model real-life systems or man-made devices, both nonlinear and dynamic properties need to be taken into account. The gen...
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry,...
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations...
Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensivecoverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments.
The first three chapters expose theconnections between the asymptotic properties of estimators in parametric models and...
Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensivecov...
The book dwells mainly on the optimality aspects of mixture designs. As mixture models are a special case of regression models, a general discussion on regression designs has been presented, which includes topics like continuous designs, de la Garza phenomenon, Loewner order domination, Equivalence theorems for different optimality criteria and standard optimality results for single variable polynomial regression and multivariate linear and quadratic regression models. This is followed by a review of the available literature on estimation of parameters in mixture models. Based on recent...
The book dwells mainly on the optimality aspects of mixture designs. As mixture models are a special case of regression models, a general discussi...
