Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K})$ is possibly much larger than the ...

Generally speaking, there are two types of outcomes (i.e. response) in statistical analysis: continuous and categorical responses. Linear Models (LM) are one of the most commonly used statistical ...

Multiparameter likelihood models (MLMs) with multiple covariates have a wide range of applications; however, they encounter the "curse of dimensionality" problem when the dimension of the covariates ...

This course is compulsory on the BSc in Financial Mathematics and Statistics and BSc in Statistics with Finance. This course is available on the BSc in Actuarial Science, BSc in Business Mathematics ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of ...

Suppose that D 0 is the deviance resulting from fitting a generalized linear model and that D 1 is the deviance from fitting a submodel. Then, under appropriate regularity conditions, the asymptotic ...

In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...