The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. New developments in the R-INLA are formalized and it is shown how these...

The Pairwise Markov Chain (PMC) model assumes the couple of observations and states processes to be a Markov chain. To extend the modeling capability of class-conditional densities involved in the PMC model, copulas are introduced and the influence of their shape on classification error rates is...

In this paper, we present a novel sufficient dimension reduction method for multivariate regressions with categorical predictors. We adopt ideas from a previous work byChiaromonte et al. (2002) who proposed sufficient dimension reduction in regressions with categorical predictors and the work...

We propose a linear regression model with slash-elliptical errors. The slash-elliptical distribution with parameter q is defined as the ratio of two independent random variables Z and U1q, where Z has elliptical distribution and U has uniform distribution in (0,1). The main feature of the...

We introduce a general method for obtaining more flexible new distributions by compounding the extended Weibull and power series distributions. The compounding procedure follows the same set-up carried out by Adamidis and Loukas (1998) and defines 68 new sub-models. The new class of generated...

Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method, MAVE (minimum average variance...

The intention is to provide a Bayesian formulation of regularized local linear regression, combined with techniques for optimal bandwidth selection. This approach arises from the idea that only those covariates that are found to be relevant for the regression function should be considered by the...

A variable selection problem is analysed for use in Principal Component Analysis (PCA). In this case, the set of original variables is divided into disjoint groups. The problem resides in the selection of variables, but with the restriction that the set of variables that is selected should...

The problem of variable selection for linear regression in a high dimension model is considered. A new method, called Extended-VISA (Ext-VISA), is proposed to simultaneously select variables and encourage a grouping effect where strongly correlated predictors tend to be in or out of the model...

Three recent nonparametric methodologies for estimating a monotone regression function F and its inverse F−1 are (1) the inverse kernel method DNP (Dette et al., 2005; Dette and Scheder, 2010), (2) the monotone spline (Kong and Eubank (2006)) and (3) the data adaptive method NAM (Bhattacharya and...