منابع مشابه
Inconsistency Between Univariate and Multiple Logistic Regressions
Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression and from the multiple logistic regression tend to be conflicting. A covariate may show very strong effect on the outcome in the multiple regression but not in t...
متن کاملDeletion diagnostics for alternating logistic regressions.
Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equ...
متن کاملGene Selection Using Logistic Regressions Based on Aic, Bic and Mdl Criteria
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables (gene expressions) and the small number of experimental conditions. Many gene-selection and classification methods have been proposed; however most of these treat gene selection and classification separately, and not under the same model. We propose a Bayesian approach to gene ...
متن کاملApplying Frequentist and Bayesian Logistic Regressions to MOOCs Data in SAS: a Case Study
Massive Open Online Courses (MOOCs) have attracted increasing attention in educational-data-mining research. MOOC platforms provide free high education courses to Internet users worldwide. However, MOOCs have high enrollment but notoriously low completion rates. The goal of this study is to use frequentist and Bayesian logistic regressions to investigate whether and how students’ engagement, in...
متن کاملLogit models and logistic regressions for social networks: II. Multivariate relations.
The research described here builds on our previous work by generalizing the univariate models described there to models for multivariate relations. This family, labelled p*, generalizes the Markov random graphs of Frank and Strauss, which were further developed by them and others, building on Besag's ideas on estimation. These models were first used to model random variables embedded in lattice...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nihon Ika Daigaku Igakkai Zasshi
سال: 2014
ISSN: 1349-8975,1880-2877
DOI: 10.1272/manms.10.186