Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the 3rd edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive presents a compr...Loe edasi...
Intended to provide a nearly comprehensive account of methods, theory, applications, as well as open problems related to robust SAE, that the monograph will help persuade practitioners, such as those in government agencies, to more readily adopt r...Loe edasi...
Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensiv...Loe edasi...
Nonregular factorial designs are a class of factorial designs that enable researchers to study simultaneously the effects of many explanatory variables on a response variable of interest. Factorial designs have been in the mainstream of design res...Loe edasi...
Statistics has developed as a field through seminal ideas and fascinating controversies. This book concerns a wide-ranging set of 13 important statistical topics, grouped into three general areas: Philosophical Approaches to Statistical Inference; I...Loe edasi...
Statistics has developed as a field through seminal ideas and fascinating controversies. This book concerns a wide-ranging set of 13 important statistical topics, grouped into three general areas. Statistics has developed as a field...Loe edasi...
Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. This book presents an overview of the theory and applications of p...Loe edasi...
This book provides a thorough treatment of QLS regression—a computational approach for the estimation of correlation parameters within the framework of GEEs. Special focus is given to goodness-of-fit analysis as well as new strategies for selectin...Loe edasi...
There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models bas...Loe edasi...
This self-contained book presents up-to-date hypothesis testing methods for functional data analysis. Useful for statistical researchers and practitioners analyzing functional data, it gives both a theoretical and applied treatment of functional d...Loe edasi...
Design and Analysis of Cross-Over Trials is concerned with a specific kind of comparative trial known as the cross-over trial, in which subjects receive different sequences of treatments. Such trials are widely used in clinical an...Loe edasi...
This gives a comprehensive introduction to the (standard) statistical analysis based on the theory of martingales and develops entropy methods in order to treat dependent data in the framework of martingales. The author starts a summary of the martin...Loe edasi...
Object Oriented Data Analysis (OODA) provides a useful general framework for the consideration of many types of Complex Data. It is deliberately intended to be particularly useful in the analysis of data in complicated situations which are typical...Loe edasi...
Valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a supplementary text for courses on data mining, se...Loe edasi...
Functional Data Analysis with R is primarily aimed at undergraduate, masters, and PhD students, as well as data scientists and researchers working on functional data analysis. The book can be read at different levels and combines state-of-the-art so...Loe edasi...
Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay...Loe edasi...
This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data i...Loe edasi...
This book presents an overview of the diverse literature on change-point methodology and describes some unifying themes and recent breakthroughs. It can be used as a second-year Ph.D. level course in statistics. It is expected to be useful to rese...Loe edasi...
Research on mixed models has been extensive over the most recent decade. This book differs from the authors previous monographs on longitudinal data in that it focuses on mixed models of a linear, generalized linear and nonlinear type. The book p...Loe edasi...
Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions. The name energy derives from Newtons gravitational potential energy, and there is an elegant relation to the notio...Loe edasi...
