Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR)

Vita Ratnasari*, Purhadi, Marisa Rifada, Andrea Tri Rian Dani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Logit regression (or logistic regression) is a statistical analysis of categorical data. The binary responses have two categories. We present the Bivariate Polynomial Binary Logit Regression (BPBLR), which extends logit regression by modeling two correlated binary response variables. This model uses a polynomial pattern to capture the association between the logit and predictor variables. The maximum likelihood estimation (MLE) method is used for parameter estimation, and the maximum likelihood ratio test (MLRT) method is used for the statistical testing of the proposed model. The distribution of the test statistics asymptotically is the Chi-square distribution. Selection of the optimal polynomial degree and the best model is based on the minimum Deviance value. Some highlights of the proposed method are: • Statistical modeling innovation on categorical data with two correlated binary response variables, namely Bivariate Polynomial Binary Logit Regression (BPBLR). • The statistical test is obtained using MLRT method.

Original languageEnglish
Article number103099
JournalMethodsX
Volume14
DOIs
Publication statusPublished - Jun 2025
Externally publishedYes

Keywords

  • Binary responses
  • Bivariate
  • Logit regression
  • Polynomial
  • Poverty

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