NWP-based lightning prediction using flexible count data regression

A Flexible Regression Model for Count Data

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often overor under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway–Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson r...

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...

Prediction-based regularization using data augmented regression

The role of regularization is to control fitted model complexity and variance by penalizing (or constraining) models to be in an area of model space that is deemed reasonable. This is typically achieved by penalizing a parametric or non-parametric representation of the model. In this paper we advocate the use of prior knowledge or expectations about the predictions of models for regularization....

Essentials of Count Data Regression

Regression Analysis of Count Data

Three nonlinear count models, Poisson R.egression (PR), Negative Binomial Regression (NBR), and Generalized Poisson Regression (GPR) are used for assessing the effects of risk factors on agricultural injuries from farm injury data. A sample of 1,322 respondents who participated in the farm safety/injury baseline survey in nine rural counties in Alabama and Mississippi, aged 18 years and older a...