Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data

Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data

This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small n...

Estimation of Count Data using Bivariate Negative Binomial Regression Models

Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...

The Comparison Between Goodness of Fit Tests for Copula

‎Copula functions as a model can show the relationship between variables‎. ‎Appropriate copula function for a specific application is a function that shows the dependency between data in a best way‎. ‎Goodness of fit tests theoretically are the best way in selection of copula function‎. ‎Different ways of goodness of fit for copula exist‎. ‎In this paper we will examine the goodness of fit test...

Goodness-of-Fit Tests for Parametri Regression Models

Goodness-of-Fit Tests for Parametric Regression Models

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric Ž ts is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates...