This book provides an account of the theory and applications of multivariate reduced-rank regression, a tool of multivariate analysis that recently has come into increased use in broad areas of applic
Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoot
This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity. The proposed techniques are impl
Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent vari
Approximation by Multivariate Singular Integrals is the first monograph to illustrate the approximation of multivariate singular integrals to the identity-unit operator. The basic approximation proper
This text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. This is done
This book offers a new, fairly efficient, and robust alternative to analyzing multivariate data. The analysis of data based on multivariate spatial signs and ranks proceeds very much as does a tradit
This book presents the authors' personal selection of topics in multivariate statistical analysis with emphasis on tools and techniques. Topics included range from definitions of multivariate moments,
Wildlife researchers and ecologists make widespread use of multivariate statistics in their studies. With its focus on the practical application of the techniques of multivariate statistics, this bo
Wildlife researchers and ecologists make widespread use of multivariate statistics in their studies. With its focus on the practical application of the techniques of multivariate statistics, this boo
This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the m
This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides a
This booktraces the theory and methodology of multivariate statistical analysis andshows how it can be conducted in practice using the LISREL computer program. Itpresents not only the typical uses of
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using clim
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, share
Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitione
Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate st
This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear
This fully revised third edition introduces geostatistics by emphasising the multivariate aspects for scientists, engineers and statisticians. Geostatistics offers a variety of models, methods and tec
This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear
Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to e
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model
This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric proced
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniq
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error dist
This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what mode
This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms
Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions
This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from technique
The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivaria
When initially introduced, the reduced density matrices (RDMs) held the promise of greatly simplifying electronic structure calculations. Although these expectations were not met immediately, recen
The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and method
This book provides a balanced, modern introduction to Bayesian and frequentist methods for regression analysis. The author discusses Frequentist and Bayesian Inferences; Linear Models; Binary Data Mod
An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interest
This monograph is multivariate, multi-perspective and multipurpose. We intend to be innovatively integrative through statistical synthesis. Innovation requires capacity to operate in ways that are n
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for
The book focuses on the physical and mathematical foundations of model-based turbulence control: reduced-order modelling and control design in simulations and experiments. Leading experts provide elem
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. T
This book focuses on tools and techniques for building valid regression models using real-world data. A key theme throughout the book is that it only makes sense to base inferences or conclusions on v
This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences o
