We consider the problem of gene selection and classification based on the expression data. Specifically, we propose a bootstrap Bayesian gene selection method for nonlinear probit regression. A binomial probit regression model with data augmentation is used to transform the binomial problem into a sequence of smoothing problems. The probit regressor is approximated as a nonlinear combination of...

This article focuses on the estimation of population proportion when the study variable is sensitive in nature. Two implicit randomized response techniques are proposed where the unrelated trait can be chosen subjectively. In addition to unbiased estimation of population proportion and variance, an empirical study is conducted to inspect the relative efficiency facet of the proposed techniques....

In recent years, numerous researches have been carried out with purpose of predicting motor vehicle crashes on transportation facilities as freeways and urban or rural highways. Accident process can be modeled successfully with assuming a dual-state data-generating process. Based on this assumption, road components like intersections or road segments have two states of perfectly safe and unsafe...

The relationship between genetic variability and individual phenotypes is usually investigated by testing for association relying on called genotypes. Allele counts obtained from next-generation sequence data could be used for this purpose too. Genetic association can be examined by treating alternative allele counts (AACs) as the response variable in negative binomial regression. AACs from seq...