MOTIVATION A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessi...

MOTIVATION In a gene regulatory network, genes are typically regulated by transcription factors (TFs). Transcription factor activity (TFA) is more difficult to measure than gene expression levels are. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. Th...

We study generalized varying coefficient partially linearmodels when some linear covariates are error prone, but their ancillary variables are available. We first calibrate the error-prone covariates, then develop a quasi-likelihood-based estimation procedure. To select significant variables in the parametric part, we develop a penalized quasi-likelihood variable selection procedure, and the re...

Ala~traet--Errors-in-variables problems are considered for the case of three variables, where the underlying relations among the noise-free variables are linear, and covariance information for the noisy variables is available. The static problem where the variables are complex (which can arise when narrowband filtering is used) is analysed in detail. Some results are also presented for the case...

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our app...

OBJECTIVES The aim of this study was to explore the relationship between socio-demographic characteristics and the level of complementary and supplementary person-organization fit (P-O fit). MATERIAL AND METHODS The study sample was a group of 600 Polish workers, urban residents aged 19-65. Level of P-O fit was measured using the Subjective Person-Organization Fit Questionnaire by Czarnota-Bo...

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in...

This study proposes a new approach for identifying critical factors that affect mobile service diffusion. To this end, the Bass model and supplementary statistical method are adopted. Initially, the Bass model parameters are derived from monthly user data of mobile service and considered as dependent variables. Candidate factors of mobile services diffusion, collected from service description d...