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An Introduction to Generalized Additive Models with R

Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and;

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Generalized Linear Mixed Models

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the;

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Foundations of Linear and Generalized Linear Models

introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods * An overview;

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An Introduction to Generalized Linear Models

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive;

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Generalized, Linear, and Mixed Models

An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models;

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Introduction to General and Generalized Linear Models

Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models;

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A First Course in Linear Model Theory

Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an;

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An Introduction to Generalized Linear Models

An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on;

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An Introduction to Generalized Linear Models

An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on;

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Generalized Linear Models with Random Effects

Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models;

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Generalized Additive Models

missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range;

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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Interaction Effects in Linear and Generalized Linear Models: Examples and Applications Using Stata

Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a;

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Generalized Linear Models

Explaining the theoretical underpinning of generalized linear models, this text enables researchers to decide how to select the best way to;

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Generalized Linear Models With Examples in R

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it;

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Linear Algebra and Linear Models

problems on rank: the matrix rank is an important and rich topic with connections to many aspects of linear algebra such as generalized inverses;

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A Generalized Framework of Linear Multivariable Control

A Generalized Framework of Linear Multivariable Control proposes a number of generalized models by using the generalized inverse of matrix;

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Generalized Linear Models

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective;

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Multivariate Generalized Linear Mixed Models Using R

members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After;

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Statistics for Linguists

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple;

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Statistics for Linguists

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple;

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Generalized Additive Models

the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment;

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data;

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Generalized Linear Models

The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to;

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