Autologistic Regression Models for Spatio-Temporal Binary Data

2016 ◽  
pp. 387-406
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Autologistic Regression ◽  
Spatio Temporal

scholarly journals A joint regression modeling framework for analyzing bivariate binary data in R

2017 ◽  
Vol 5 (1) ◽  
pp. 268-294 ◽  
Author(s):  
Giampiero Marra ◽  
Rosalba Radice
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Joint Modeling ◽  
Data Sets ◽  
Modeling Framework ◽  
Flexible Joint ◽  
Sampling Schemes ◽  
Multivariate Response ◽  
Interpretable Models ◽  
Non Gaussian

Abstract We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.

Comparison of alternative regression models for paired binary data

1994 ◽  
Vol 13 (10) ◽  
pp. 1023-1036 ◽  
Author(s):  
Robert J. Glynn ◽  
Bernard Rosner
Keyword(s):  
Binary Data ◽  
Regression Models

Bayesian analyses of longitudinal binary data using Markov regression models of unknown order

2001 ◽  
Vol 20 (5) ◽  
pp. 755-770 ◽  
Author(s):  
Alaattin Erkanli ◽  
Refik Soyer ◽  
Adrian Angold
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Bayesian Analyses ◽  
Longitudinal Binary Data

scholarly journals Prevalence of Occupation Associated with Increased Mobility During COVID-19 Pandemic

2020 ◽  
Author(s):  
Enbal Shacham ◽  
Stephen Scroggins ◽  
Matthew Ellis
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Food Service ◽  
Service Workers ◽  
Census Tracts ◽  
Service Business ◽  
Mobility Data ◽  
Spatio Temporal ◽  
Food Service Workers ◽  
Essential Food

AbstractPurposeIdentifying geographic-level prevalence of occupations associated with mobility during local stay-at-home pandemic mandate.MethodsA spatio-temporal ecological framework was applied to determine census-tracts that had significantly higher rates of occupations likely to be deemed essential: food-service, business and finance, healthcare support, and maintenance. Real-time mobility data was used to determine the average daily percent of residents not leaving their place of residence. Spatial regression models were constructed for each occupation proportion among census-tracts within a large urban area.ResultsAfter adjusting for demographics, results indicate census-tracts with higher proportion of food-service workers, healthcare support employees, and office administration staff are likely to have increased mobility.ConclusionsIncreased mobility among communities is likely to exacerbate COVID-19 mitigation efforts. This increase in mobility was also found associated with specific demographics suggesting it may be occurring among underserved and vulnerable populations. We find that prevalence of essential employment presents itself as a candidate for driving inequity in morbidity and mortality of COVID-19.

Sign in / Sign up

Export Citation Format

Share Document