Discriminant analysis of multivariate survival data based on copulas

2017 Fiscal Year Final Research Report

• Project/Area Number: 25330031
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Statistical science
• Research Institution: Hokkaido University
• Principal Investigator: Suzukawa Akio 北海道大学, 公共政策学連携研究部, 教授 (00277287)
• Project Period (FY): 2013-04-01 – 2018-03-31
• Keywords: 生存時間データ / 判別分析 / コピュラ / 打切りデータ

Outline of Final Research Achievements

Multivariate survival time distribution was modeled using copula (a function that connects multidimensional probability distribution and its marginal distributions). Based on this model, we developed a discriminant analysis method of multivariate survival time data.In particular, focusing on the application range to medical data, we developed discriminant analysis methods applicable to actual multivariate survival time data in medical research In data analysis of multivariate survival time, censoring of data and non-multivariate normality are important problems. We proposed a parametric model for censoring patterns. Based on copula modeling of multivariate survival distributions, we developed flexible discriminant analysis methods.

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