Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR)
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124005508 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846113177581912064 |
|---|---|
| author | Vita Ratnasari Purhadi Marisa Rifada Andrea Tri Rian Dani |
| author_facet | Vita Ratnasari Purhadi Marisa Rifada Andrea Tri Rian Dani |
| author_sort | Vita Ratnasari |
| collection | DOAJ |
| description | 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. • The BPBLR model is applied to actual datasets regarding the depth and severity of poverty to capture poverty problems SDGs 1 |
| format | Article |
| id | doaj-art-e48a46971ef6434b9cd2520575a39cd3 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-e48a46971ef6434b9cd2520575a39cd32024-12-22T05:28:08ZengElsevierMethodsX2215-01612025-06-0114103099Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR)Vita Ratnasari0 Purhadi1Marisa Rifada2Andrea Tri Rian Dani3Corresponding author.; Sepuluh Nopember Institute of Technology, Airlangga University, Mulawarman University, IndonesiaSepuluh Nopember Institute of Technology, Airlangga University, Mulawarman University, IndonesiaSepuluh Nopember Institute of Technology, Airlangga University, Mulawarman University, IndonesiaSepuluh Nopember Institute of Technology, Airlangga University, Mulawarman University, IndonesiaLogit 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. • The BPBLR model is applied to actual datasets regarding the depth and severity of poverty to capture poverty problems SDGs 1http://www.sciencedirect.com/science/article/pii/S2215016124005508The Bivariate Polynomial Logit Regression Models |
| spellingShingle | Vita Ratnasari Purhadi Marisa Rifada Andrea Tri Rian Dani Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR) MethodsX The Bivariate Polynomial Logit Regression Models |
| title | Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR) |
| title_full | Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR) |
| title_fullStr | Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR) |
| title_full_unstemmed | Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR) |
| title_short | Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR) |
| title_sort | explore poverty with statistical modeling the bivariate polynomial binary logit regression bpblr |
| topic | The Bivariate Polynomial Logit Regression Models |
| url | http://www.sciencedirect.com/science/article/pii/S2215016124005508 |
| work_keys_str_mv | AT vitaratnasari explorepovertywithstatisticalmodelingthebivariatepolynomialbinarylogitregressionbpblr AT purhadi explorepovertywithstatisticalmodelingthebivariatepolynomialbinarylogitregressionbpblr AT marisarifada explorepovertywithstatisticalmodelingthebivariatepolynomialbinarylogitregressionbpblr AT andreatririandani explorepovertywithstatisticalmodelingthebivariatepolynomialbinarylogitregressionbpblr |