Logistic regression is a simple statistical tool used in binary classification. LR is useful for situations in which we want to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. Unlike LR, PCA is another technique used in feature extraction and in dimensionality reduction. This paper puts forth the experimental results of LR model an...

aim : the aim of this study was to compare alternatives methods for analysis of zero inflated count data and compare them with simple count models that are used by researchers frequently for such zero inflated data. background : analysis of viral load and risk factors could predict likelihood of achieving sustain virological response (svr). this information is useful to protect a person from ac...

In this paper, a new bivariate negative binomial regression (BNBR) model allowing any type of correlation is defined and studied. The marginal means of the bivariate model are functions of the explanatory variables. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Some test statistics including goodness-of-fit are discussed. Two numerical da...

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

We study generalized linear models for time series of counts, where serial dependence is introduced through a dependent latent process in the link function. Conditional on the covariates and the latent process, the observation is modelled by a negative binomial distribution. To estimate the regression coefficients, we maximize the pseudolikelihood that is based on a generalized linear model wit...