印度尼西亚贫困模型的 GLMM 和 GLMMTree

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI:10.34123/icdsos.v2023i1.333

Suseno Bayu, K. Notodiputro, B. Sartono

{"title":"印度尼西亚贫困模型的 GLMM 和 GLMMTree","authors":"Suseno Bayu, K. Notodiputro, B. Sartono","doi":"10.34123/icdsos.v2023i1.333","DOIUrl":null,"url":null,"abstract":"GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMM trees in real data applications of poverty in Indonesia. Using this data, we found that the GLMMTree algorithm method performs similarly to GLMM. 2 significant predictors affect poverty in Indonesia: the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding two variables, namely the percentage of households with electricity lighting access and the percentage of households with clean drinking water sources, that interact with predictor variables in the model.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"3 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GLMM and GLMMTree for Modelling Poverty in Indonesia\",\"authors\":\"Suseno Bayu, K. Notodiputro, B. Sartono\",\"doi\":\"10.34123/icdsos.v2023i1.333\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMM trees in real data applications of poverty in Indonesia. Using this data, we found that the GLMMTree algorithm method performs similarly to GLMM. 2 significant predictors affect poverty in Indonesia: the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding two variables, namely the percentage of households with electricity lighting access and the percentage of households with clean drinking water sources, that interact with predictor variables in the model.\",\"PeriodicalId\":151043,\"journal\":{\"name\":\"Proceedings of The International Conference on Data Science and Official Statistics\",\"volume\":\"3 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The International Conference on Data Science and Official Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34123/icdsos.v2023i1.333\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The International Conference on Data Science and Official Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34123/icdsos.v2023i1.333","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

GLMMTree 是一种基于树的算法，可以检测交互作用并在 GLMM 中找到子组，从而改进固定效应估计。本研究将 GLMM 树应用于印尼贫困问题的真实数据中。通过使用这些数据，我们发现 GLMMTree 算法方法的表现与 GLMM 相似。有两个重要的预测因素影响着印度尼西亚的贫困状况：失业率和不变价格下的 GRDP。GLMMTree 算法通过找到与模型中的预测变量相互作用的两个变量，即有电照明的家庭百分比和有清洁饮用水源的家庭百分比，丰富了分析内容。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

GLMM and GLMMTree for Modelling Poverty in Indonesia

GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMM trees in real data applications of poverty in Indonesia. Using this data, we found that the GLMMTree algorithm method performs similarly to GLMM. 2 significant predictors affect poverty in Indonesia: the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding two variables, namely the percentage of households with electricity lighting access and the percentage of households with clean drinking water sources, that interact with predictor variables in the model.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of The International Conference on Data Science and Official Statistics

自引率

0.00%

发文量