This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the glm routine package in R. Particularly, regression models will be analyzed for those cases in which the explained variable follows a Poisson or a Negative Binomial distribution. The paper will briefly present the GLM methodology for count data, while the practical part will revolve around estimating and comparing models in which the response variable shows the number of claims in a portfolio of automobile insurance policies
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...
Practical and rigorous, this book treats GLMs, covers all standard exponential family distributions,...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...
This textbook presents an introduction to multiple linear regression, providing real-world data sets...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
The analysis of residuals can capture departures from a parametrized model. In this thesis we look a...
An important statistical development in the last four decades has been the advancement in the field ...
In this thesis we describe the theory of generalized linear models and demon- strate its application...
In this thesis we describe the theory of generalized linear models and demon- strate its application...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR ...
WOS: 000457437600004In order to combat multicollinearity, the r - d class estimator was introduced i...
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound mode...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...
Practical and rigorous, this book treats GLMs, covers all standard exponential family distributions,...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...
This textbook presents an introduction to multiple linear regression, providing real-world data sets...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
The analysis of residuals can capture departures from a parametrized model. In this thesis we look a...
An important statistical development in the last four decades has been the advancement in the field ...
In this thesis we describe the theory of generalized linear models and demon- strate its application...
In this thesis we describe the theory of generalized linear models and demon- strate its application...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR ...
WOS: 000457437600004In order to combat multicollinearity, the r - d class estimator was introduced i...
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound mode...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...
Practical and rigorous, this book treats GLMs, covers all standard exponential family distributions,...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...