Variable selection via penalized estimation is appealing for dimension reduction. For penalized linear regression, Efron, et al. (2004) introduced the LARS algorithm. Recently, the coordinate descent (CD) algorithm was developed by Friedman, et al. (2007) for penalized linear regression and penalized logistic regression and was shown to gain computational superiority. This paper explores...

BACKGROUND In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression...

Motivated by the newly developed max-linear competing copula factor models and max-stable nonlinear time series models, we propose a new class of regression to take advantages easy interpretable features embedded in linear models. It can be seen that relation is special case relation. We develop an EM algorithm based maximum likelihood estimation procedure. The consistency asymptotics estimator...

Field observations form the basis of many scientific studies, especially in ecological and social sciences. Despite efforts to conduct such surveys a standardized way, can be prone systematic measurement errors. The removal variability introduced by observation process, if possible, greatly increase value this data. Existing non-parametric techniques for correcting errors assume linear additive...

BACKGROUND Health-related quality of life (HRQL) has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the scale and often highly skewed. Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less w...