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

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  • 1Sepuluh Nopember Institute of Technology, Airlangga University, Mulawarman University, Indonesia.

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Summary
This summary is machine-generated.

We introduce the Bivariate Polynomial Binary Logit Regression (BPBLR) for analyzing two related binary outcomes. This statistical method enhances logit regression for complex categorical data analysis, aiding in poverty assessment.

Keywords:
Binary responsesBivariateLogit regressionPolynomialPovertyThe Bivariate Polynomial Logit Regression Models

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Area of Science:

  • Statistics
  • Econometrics
  • Social Sciences

Background:

  • Logit regression is a standard statistical method for analyzing categorical data, particularly binary responses.
  • Existing models often struggle to capture the correlation between multiple binary outcome variables effectively.

Purpose of the Study:

  • To introduce the Bivariate Polynomial Binary Logit Regression (BPBLR) model.
  • To extend logit regression for modeling two correlated binary response variables using a polynomial pattern.
  • To apply the BPBLR model to real-world poverty data for Sustainable Development Goals (SDGs) 1.

Main Methods:

  • The Bivariate Polynomial Binary Logit Regression (BPBLR) model is proposed, incorporating a polynomial pattern to describe the association between correlated binary responses and predictor variables.
  • Parameter estimation is performed using the Maximum Likelihood Estimation (MLE) method.
  • Statistical testing of the model is conducted using the Maximum Likelihood Ratio Test (MLRT), with test statistics asymptotically following a Chi-square distribution.
  • Model selection and optimal polynomial degree determination are based on minimizing the Deviance value.

Main Results:

  • The BPBLR model provides a novel approach to statistical modeling for categorical data with two correlated binary response variables.
  • The MLRT method is utilized for robust statistical testing of the proposed model.
  • The application to poverty datasets demonstrates the model's utility in analyzing the depth and severity of poverty, contributing to SDG 1 objectives.

Conclusions:

  • The BPBLR model offers a significant statistical modeling innovation for handling correlated binary outcomes.
  • The developed methodology, including MLE and MLRT, provides a comprehensive framework for analysis and testing.
  • The successful application to poverty data highlights the model's practical relevance in addressing complex social issues and supporting global development goals.