Estimating Conditional Event Probabilities with Mixed Regressors: a Weighted Nearest Neighbour Approach

IF 0.3 Q4 ECONOMICS
M. Khatun, S. Siddiqui
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引用次数: 0

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.
用混合回归估计条件事件概率:一种加权最近邻方法
k近邻方法是一种流行的非参数技术,用于解决分类和回归问题,而不必对所研究的统计关系的函数形式做出潜在的限制性先验假设。本文的目的是证明这种方法的范围可以扩展,从而能够同时考虑连续、有序离散和无序离散的解释变量。公开可用数据集的示例性应用证明了所提出方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.60
自引率
0.00%
发文量
23
审稿时长
24 weeks
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