Work

Redundancy test for some variables in linear discriminant analysis with two-step monotone incomplete data

Year: 2024

Type: article

Abstract: We evaluated the null hypothesis such that a set of variables is redundant in linear discriminant analysis using two-step monotone incomplete data. We derived a likelihood ratio test statistic and proposed a corrected likelihood ratio test statistic via the Bartlett correction. Finally, we evaluated the proposed test statistic and compared it with the likelihood ratio test statistic using Monte Carlo simulations. The results show that the proposed test statistic can control type I errors more accurately than the likelihood ratio test.

Source: SUT Journal of Mathematics

DOI: 10.55937/sut/1729410030

Authors: Yuki Okada, Naoki Matsuchi, Masashi Hyodo, Nobumichi Shutoh

Institutions: Kobe University, Oita University of Nursing and Health Sciences, Kanagawa University

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funder: Japan Society for the Promotion of Science

Award: 特殊な共分散構造に対処する新たな高次元統計理論の構築