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Generalized Additive Models

This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These;

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Extending The Linear Model With R

. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data;

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Nonparametric Regression and Generalized Linear Models

problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and;

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Nonparametric Regression and Spline Smoothing

in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and;

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Nonparametric Regression and Spline Smoothing

in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and;

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Confidence Intervals in Generalized Regression Models

regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox;

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Confidence Intervals in Generalized Regression Models

regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox;

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Analysis of Variance, Design, and Regression

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling;

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Analysis of Variance, Design, and Regression

using methods for unbalanced data Introductions to nonparametric and lasso regression Introductions to general additive and generalized;

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Applied Regression Analysis and Generalized Linear Models

Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the;

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Partially Linear Models

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing;

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Regression: Models, Methods and Applications

extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression;

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Extending the Linear Model with R

. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods;

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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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Regression Analysis and Linear Models

, generalized linear model, etc. This book presents the complex subject of regression analysis and linear models, in the most comprehensible and easy to;

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Observed Confidence Levels

associated with smoothing methods, including nonparametric density estimation and regression. The author also describes applications in generalized;

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Observed Confidence Levels

associated with smoothing methods, including nonparametric density estimation and regression. The author also describes applications in generalized;

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Simultaneous Inference in Regression

simple regression line, a multiple linear regression model, and polynomial regression models. He also uses simultaneous confidence bands to;

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Simultaneous Inference in Regression

simple regression line, a multiple linear regression model, and polynomial regression models. He also uses simultaneous confidence bands to;

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An Introduction to Generalized Linear Models

of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial;

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An Introduction to Generalized Linear Models

of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial;

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An Introduction to Generalized Additive Models with R

splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the;

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Cambridge Series in Statistical and Probabilistic Mathematics

on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework;

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Econometrics

of nonparametric methods for estimating density and regression of functions aeo Emphasis on diagnostic procedures and on prediction criteria for evaluating;

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Applied Linear Models with SAS

models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric;

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A First Course in Linear Model Theory

. Revised or expanded topics include linear fixed, random and mixed effects models, generalized linear models, Bayesian and hierarchical linear;

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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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