How does underdispersion affect inference? Is underdispersion a common problem in practice?

We prove that this optimization problem admits a tractable reformulation and encapsulates the classical as well as the popular regularized logistic regression. What is logistic regression? Logistic regression is a data analysis technique that uses mathematics to find the relationships between two data factors. It then ... Logistic regression is a predictive analysis. Logistic regression explains the relationship between a binary dependent variable and one or more independent ... Feb 16, 2001 ... First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend ... Mar 5, 2013 ... I now believe that McFadden's R 2 is a better choice. However, I've also learned about another R 2 that has good properties, a lot of intuitive appeal, and is ... Results from classic linear regression regarding the effect of adjusting for covariates upon the precision of an estimator of exposure effect are often ... Logistic regression definition: Logistic regression is a statistical model, also called the logit model, designed to find a relationship between two data ... Logistic regression analysis is used to examine the association of (categorical or continuous) independent variable(s) with one dichotomous dependent variable. Apr 15, 2019 ... Using hierarchical logistic regression to study DIF and DIF variance in multilevel data. Pre-print: Journal of Educational Measurement, v ... Mar 17, 2023 ... To create the model, declare a logistic_reg() model. This needs mixture and penalty arguments which control the amount of regularization. A ...