Restricted Parameter Space Estimation Problems


Book Description

This monograph is addressed to anyone interested in the subject of restrict- parameter-space estimation, and in particular to those who want to learn, or bring their knowledge up to date, about (in)admissibility and minimaxity problems for such parameter spaces. The coverage starts in the early 1950s when the subject of inference for - stricted parameter spaces began to be studied and ends around the middle of 2004. It presents known, and also some new, results on (in)admissibility and minimaxity for nonsequential point estimation problems in restricted ?ni- dimensional parameter spaces. Relationships between various results are d- cussed and open problems are pointed out. Few complete proofs are given, but outlines of proofs are often supplied. The reader is always referred to the published papers and often results are clari?ed by presenting examples of the kind of problems an author solves, or of problems that cannot be solved by a particular result. The monograph does not touch on the subject of testing hypotheses in - stricted parameter spaces. The latest books on that subject are by Robertson, Wright and Dykstra (1988) and Akkerboom (1990), but many new results in that area have been obtained since. The monograph does have a chapter in which questions about the existence of maximum likelihood estimators are discussed. Some of their properties are also given there as well as some algorithms for computing them. Most of these results cannot be found in the Robertson, Wright, Dykstra book.




A Festschrift for Herman Rubin


Book Description




Constrained Statistical Inference


Book Description

An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions. Chapter coverage includes: Population means and isotonic regression Inequality-constrained tests on normal means Tests in general parametric models Likelihood and alternatives Analysis of categorical data Inference on monotone density function, unimodal density function, shape constraints, and DMRL functions Bayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory







Weak Dependence: With Examples and Applications


Book Description

This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.




Advancing Women in Science


Book Description

Many countries have implemented policies to increase the number and quality of scientific researchers as a means to foster innovation and spur economic development and progress. To that end, grounded in a view of women as a rich, yet underutilized knowledge and labor resource, a great deal of recent attention has focused on encouraging women to pursue education and careers in science — even in countries with longstanding dominant patriarchal regimes. Yet, overall, science remains an area in which girls and women are persistently disadvantaged. This book addresses that situation. It bridges the gap between individual- and societal-level perspectives on women in science in a search for systematic solutions to the challenge of building an inclusive and productive scientific workforce capable of creating the innovation needed for economic growth and societal wellbeing. This book examines both the role of gender as an organizing principle of social life and the relative position of women scientists within national and international labor markets. Weaving together and engaging research on globalization, the social organization of science, and gendered societal relations as key social forces, this book addresses critical issues affecting women’s contributions and participation in science. Also, while considering women’s representation in science as a whole, examinations of women in the chemical sciences, computing, mathematics and statistics are offered as examples to provide insights into how differing disciplinary cultures, functional tasks and socio-historical conditions can affect the advancement of women in science relative to important variations in educational and occupational realities. Edited by three social scientists recognized for their expertise in science and technology policy, education, workforce participation, and stratification, this book includes contributions from an intellectually diverse group of international scholars and analysts and features compelling cases and initiatives from around the world, with implications for research, industry practice, education and policy development.




Statistical Decision Theory and Bayesian Analysis


Book Description

In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.




Dynamic Nonlinear Econometric Models


Book Description

Many relationships in economics, and also in other fields, are both dynamic and nonlinear. A major advance in econometrics over the last fifteen years has been the development of a theory of estimation and inference for dy namic nonlinear models. This advance was accompanied by improvements in computer technology that facilitate the practical implementation of such estimation methods. In two articles in Econometric Reviews, i.e., Pötscher and Prucha {1991a,b), we provided -an expository discussion of the basic structure of the asymptotic theory of M-estimators in dynamic nonlinear models and a review of the literature up to the beginning of this decade. Among others, the class of M-estimators contains least mean distance estimators (includ ing maximum likelihood estimators) and generalized method of moment estimators. The present book expands and revises the discussion in those articles. It is geared towards the professional econometrician or statistician. Besides reviewing the literature we also presented in the above men tioned articles a number of then new results. One example is a consis tency result for the case where the identifiable uniqueness condition fails.




Frontiers in Statistical Quality Control 9


Book Description

The twenty-three papers in this volume are carefully selected, reviewed and revised for this volume, and are divided into two parts: Part 1: "On-line Control" with subchapters 1.1 "Control Charts" and 1.2 "Surveillance Sampling and Sampling Plans" and Part 2:"Off-line Control".




Regression Analysis Under A Priori Parameter Restrictions


Book Description

This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. Unlike previous publications, this volume analyses the properties of regression with inequality constrains, investigating the flexibility of inequality constrains and their ability to adapt in the presence of additional a priori information The implementation of inequality constrains improves the accuracy of models, and decreases the likelihood of errors. Based on the obtained theoretical results, a computational technique for estimation and prognostication problems is suggested. This approach lends itself to numerous applications in various practical problems, several of which are discussed in detail The book is useful resource for graduate students, PhD students, as well as for researchers who specialize in applied statistics and optimization. This book may also be useful to specialists in other branches of applied mathematics, technology, econometrics and finance