We present an estimating algorithm to fit linear and generalized linear models not involving the QR decomposition. Some new R functions are presented and discussed. For large data sets, comparisons with respect to the well-known lm() and glm(), as well as to biglm() and bigglm() from the package biglm, show that the proposed functions speed up computation while preserving numerical stability and accurac
A new numerical method to solve the downdating problem (and variants thereof), namely removing the e...
The R function glm uses step-halving to deal with certain types of convergence problems when using i...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...
This is an R packge to fit (generalized) linear models to large data sets. For data loaded in R memo...
This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the gl...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
This textbook presents an introduction to multiple linear regression, providing real-world data sets...
Due to the ease of modern data collection, applied statisticians often have access to a large set of...
The computational solution of the seemingly unrelated regression model with unequal size observation...
We study robust high-dimensional estimation of generalized linear models (GLMs); where a small numbe...
This thesis presents multiple fundamental mathematical contributions to Generalized Linear Models (G...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
We intrduce a new algorithm for 1L regularized generalized linear models. The 1L regularization proc...
Computationally efficient serial and parallel algorithms for estimating the general linear model are...
A new numerical method to solve the downdating problem (and variants thereof), namely removing the e...
The R function glm uses step-halving to deal with certain types of convergence problems when using i...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
We present an estimating algorithm to fit linear and generalized linear models not involving the QR...
This is an R packge to fit (generalized) linear models to large data sets. For data loaded in R memo...
This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the gl...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
This textbook presents an introduction to multiple linear regression, providing real-world data sets...
Due to the ease of modern data collection, applied statisticians often have access to a large set of...
The computational solution of the seemingly unrelated regression model with unequal size observation...
We study robust high-dimensional estimation of generalized linear models (GLMs); where a small numbe...
This thesis presents multiple fundamental mathematical contributions to Generalized Linear Models (G...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
We intrduce a new algorithm for 1L regularized generalized linear models. The 1L regularization proc...
Computationally efficient serial and parallel algorithms for estimating the general linear model are...
A new numerical method to solve the downdating problem (and variants thereof), namely removing the e...
The R function glm uses step-halving to deal with certain types of convergence problems when using i...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...