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

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.

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 Are you a good borrower? Mining interpretable pattern structures in credit scoring

2020 ◽  
Vol 4 (3) ◽  
pp. 67-85
Author(s):  
Sergei O. Kuznetsov ◽  
Alexey Masyutin ◽  
Aleksandr Ageev
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Design Methodology ◽  
Credit Scoring ◽  
Black Box ◽  
Original Research ◽  
Content Type ◽  
Pattern Structures ◽  
Classification And Regression ◽  
The Cost

Purpose The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability. Design/methodology/approach Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm. Findings The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability. Originality/value This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.

scholarly journals Environmental, generation and policy determinants of feed-in tariff

2015 ◽  
Vol 12 (2) ◽  
Author(s):  
Antonio Romano ◽  
Giuseppe Scandurra ◽  
Alfonso Carfora
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Policy Decision ◽  
Key Factors ◽  
Feed In Tariff

In this paper we analyze the key-factors behind the adoption of the Feed-in Tariff. We propose and test two regression models for binary data: a pooling specification and a panel one. We employ a comprehensive sample of 60 countries with distinct economic structures over the period 1980–2008. Economics, environmental and generation factors are used as regressors and results demonstrate that these factors are relevant for the policy decision to adopt the Feed-in Tariff. Furthermore, the panel specification appears a better specification, in a such heterogeneous context, than the classical pooled specification.

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