Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling

Generalized Lasso Regularization for Regression Models

A Generalized Linear Statistical Model Approach to Monitor Profiles

Statistical process control methods for monitoring processes with univariate ormultivariate measurements are used widely when the quality variables fit to known probabilitydistributions. Some processes, however, are better characterized by a profile or a function of qualityvariables. For each profile, it is assumed that a collection of data on the response variable along withthe values of the c...

Generalized LASSO with under-determined regularization matrices

This paper studies the intrinsic connection between a generalized LASSO and a basic LASSO formulation. The former is the extended version of the latter by introducing a regularization matrix to the coefficients. We show that when the regularization matrix is even- or under-determined with full rank conditions, the generalized LASSO can be transformed into the LASSO form via the Lagrangian frame...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Non-linear statistical downscaling over Europe

Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe M. Vrac, D. Paillard, and P. Naveau Laboratoire des Sciences du Climat et de l’Environnement, LSCE, CEA/CNRS/UVSQ, Institut Pierre Simon Laplace (IPSL), Centre d’étude de Saclay, Orme des Merisiers, 91191 Gif-Sur-Yvette, France Received: 20 June 2007 – Accepted: 20 June 2007 – Published: 6 July 2007...