{"title":"用混合回归估计条件事件概率:一种加权最近邻方法","authors":"M. Khatun, S. Siddiqui","doi":"10.54694/stat.2022.45","DOIUrl":null,"url":null,"abstract":"The k-Nearest Neighbour method is a popular nonparametric technique for solving classification and regression problems without having to make potentially restrictive a priori assumptions about the functional form of the statistical relationship under investigation. The purpose of this paper was to demonstrate that the scope of this method can be extended in a way that enables the simultaneous consideration of continuous, ordered discrete, and unordered discrete explanatory variables. An exemplary application to a publicly available dataset demonstrated the feasibility of the proposed approach.","PeriodicalId":43106,"journal":{"name":"Statistika-Statistics and Economy Journal","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating Conditional Event Probabilities with Mixed Regressors: a Weighted Nearest Neighbour Approach\",\"authors\":\"M. Khatun, S. Siddiqui\",\"doi\":\"10.54694/stat.2022.45\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The k-Nearest Neighbour method is a popular nonparametric technique for solving classification and regression problems without having to make potentially restrictive a priori assumptions about the functional form of the statistical relationship under investigation. The purpose of this paper was to demonstrate that the scope of this method can be extended in a way that enables the simultaneous consideration of continuous, ordered discrete, and unordered discrete explanatory variables. An exemplary application to a publicly available dataset demonstrated the feasibility of the proposed approach.\",\"PeriodicalId\":43106,\"journal\":{\"name\":\"Statistika-Statistics and Economy Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2023-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistika-Statistics and Economy Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54694/stat.2022.45\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistika-Statistics and Economy Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54694/stat.2022.45","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
Estimating Conditional Event Probabilities with Mixed Regressors: a Weighted Nearest Neighbour Approach
The k-Nearest Neighbour method is a popular nonparametric technique for solving classification and regression problems without having to make potentially restrictive a priori assumptions about the functional form of the statistical relationship under investigation. The purpose of this paper was to demonstrate that the scope of this method can be extended in a way that enables the simultaneous consideration of continuous, ordered discrete, and unordered discrete explanatory variables. An exemplary application to a publicly available dataset demonstrated the feasibility of the proposed approach.