A First Course in Linear Model Theory

This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the author's introduces students to the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models\nA First Course in Linear Model Theory systematically presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective. Through the concepts and tools of matrix and linear algebra and distribution theory, it provides a framework for understanding classical and contemporary linear model theory. It does not merely introduce formulas, but develops in students the art of statistical thinking and inspires learning at an intuitive level by emphasizing conceptual understanding.\nThe authors' fresh approach, methodical presentation, wealth of examples, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models.

Science & Math

Ravishanker, Nalini

Dey, Dipak K.

Mathematics

Chapman and Hall/CRC

Product Description Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework.The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series.Key Features:Introduction and overview of R-INLA for time series analysis.Gaussian and non-Gaussian state space models for time series.State space models for time series with exogenous predictors.Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-temporal dependence. About the Author Nalini Ravishanker is a professor in the Department of Statistics at the University of Connecticut, Storrs, USA.Balaji Raman is a statistician at Cogitaas AVA, Mumbai, India.Refik Soyer is a professor in the Department of Decision Sciences at The George Washington University, Washington D.C., USA.

Dynamic Time Series Models using R-INLA: An Applied Perspective

Raman, Balaji

Soyer, Refik

This book represents an entirely fresh approach for introducing to students the mathematical and statistical concepts and tools that form the backbone for the study of the theory and applications of linear models, both univariate and multivariate. This second edition features several new topics that are extremely relevant to the current research in statistical methodology. Revised or expanded topics include analysis of covariance, Bayesian linear and generalized linear model, nonlinear regression, ridge regression, resistant and robust regression, generalized linear models, model selection, multiple comparisons, hierarchical linear models, random effects and variance components.

A First Course in Linear Model Theory (Chapman & Hall/CRC Texts in Statistical Science)

Chi, Zhiyi

This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the author's introduces students to the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models A First Course in Linear Model Theory systematically presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective. Through the concepts and tools of matrix and linear algebra and distribution theory, it provides a framework for understanding classical and contemporary linear model theory. It does not merely introduce formulas, but develops in students the art of statistical thinking and inspires learning at an intuitive level by emphasizing conceptual understanding. The authors' fresh approach, methodical presentation, wealth of examples, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models.

See the best price to sell, buy, or rent books by Ravishanker, Nalini. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Ravishanker, Nalini

See the best price to sell, buy, or rent books by Ravishanker, Nalini. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

A First Course in Linear Model Theory

Dynamic Time Series Models using R-INLA: An Applied Perspective

A First Course in Linear Model Theory (Chapman & Hall/CRC Texts in Statistical Science)

A First Course in Linear Model Theory