Multivariate Reduced-Rank Regression

This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the coefficients of interactive logistic models for a wide range

Data analysis is a vital part of science today, and in assessing quality, multivariate analysis is often necessary in order to avoid loss of essential information. Martens provides a powerful and vers

This book describes how to use the SAS System to perform a wide variety of different regression analyses, such as using various models as well as diagnosing data problems. Topics include performing li

Easy to read and comprehensive, this book presents descriptive multivariate (DMV) statistical methods using real-world problems and data sets. It offers a unique approach to integrating statistical m

Although nearly all major social science departments offer graduate students training in quantitative methods, the typical sequencing of topics generally delays training in regression analysis and oth

This book builds on John Fox's previous volume in the QASS Series, Non Parametric Simple Regression. In this monograph readers learn to estimate and plot smooth functions when there are multiple inde

Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that

Regression toward the mean is a complex statistical principle that plays a crucial role in any research involving the measurement of change. This primer is designed to help researchers more fully unde

The first edition of Theory of Rank Tests (1967) has been the precursor to a unified and theoretically motivated treatise of the basic theory of tests based on ranks of the sample observations. For m

Real-world problems and data sets are the backbone of this groundbreaking book. Applied Multivariate Statistics with SAS Software, Second Edition provides a unique approach to this topic, integrating

This extremely well-written, straightforward book gives you the flexibility to cover regression more thoroughly than do most statistics texts, without financially taxing your students.AyIt is written

This study focuses on one aspect of the republican senate the relationship between rank and participation in debate. The senate is usually pictured as a place in which a relatively small number of hig

Explores the life and career of one of Freud's most controversial disciples, analyzes his contributions to psychology, and describes how Rank has been misinterpreted

Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor. This analysis provides a comprehensive account of models a

Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor. This analysis provides a comprehensive account of models a

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analy

This revised edition presents the relevant aspects of transformational geometry, matrix algebra, and calculus to those who may be lacking the necessary mathematical foundations of applied multivariate

A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate

By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessibl

A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate

Unlike most statistical texts, this book breathes real life into multivariate analysis. Starting with a range of actual research examples in the social sciences, it demonstrates how to make the most a

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two

A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the lan

This book clearly details the theory of groups of finite Morley rank--groups which arise in model theory and generalize the concept of algebraic groups over algebraically closed fields. Written especi

A traditional approach to developing multivariate statistical theory is algebraic. Sets of observations are represented by matrices, linear combinations are formed from these matrices by multiplying t

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of

Through the use of careful explanations and examples, Berry shows the reader how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Be

Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. Härdle argues that all smoothing methods are based on a local averaging mechanism and can be seen as essentially equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this exposition, various other smoothing methods (among them splines and orthogonal polynomials) are presented and their merits discussed. All the methods presented can be understood on an intuitive level; however, exercises and supplemental materials are provided for those readers desiring a deeper un

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Mode

This monograph reviews some of the work that has been done for longitudi- nal data in the rapidly expanding field of nonparametric regression. The aim is to give the reader an impression of the basic

The authors provide a systematic treatment of the major problems involved in using regression analysis. They clearly and concisely discuss the consequences of violating the assumptions of the regress

Interpreting and Using Regression sets out the actual procedures researchers employ, places them in the framework of statistical theory, and shows how good research takes account both of st

In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás Idrobo, and Rocıìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.

This book introduces TensorFlow to people who have some knowledge of Python. Readers will learn about many “core” TensorFlow APIs, Linear Regression, Logistic Regression, and MultiLayer Perceptrons. T

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

A comprehensive and timely edition on an emerging new trend in time seriesLinear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution t

This self-contained, systematic treatment of multivariate approximation begins with classical linear approximation, and moves on to contemporary nonlinear approximation. It covers substantial new developments in the linear approximation theory of classes with mixed smoothness, and shows how it is directly related to deep problems in other areas of mathematics. For example, numerical integration of these classes is closely related to discrepancy theory and to nonlinear approximation with respect to special redundant dictionaries, and estimates of the entropy numbers of classes with mixed smoothness are closely related to (in some cases equivalent to) the Small Ball Problem from probability theory. The useful background material included in the book makes it accessible to graduate students. Researchers will find that the many open problems in the theory outlined in the book provide helpful directions and guidance for their own research in this exciting and active area.

Damodar N. Gujarati’s Linear Regression: A Mathematical Introduction presents linear regression theory in a rigorous, but approachable manner that is accessible to students in all social science

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

Low-Rank Coals for Power Generation, Fuel and Chemical Production provides a thorough introduction to lignite (brown coal) and subbituminous coals and explores how they can be used efficiently and eco