COMPONENT-BASED REGRESSION FOR HYBRID DATA

Xiaohu Jiang

doi:10.5705/ss.202023.0186

COMPONENT-BASED REGRESSION FOR HYBRID DATA

2026-01-01

0

PRE

AI

DOI：10.5705/ss.202023.0186

Original

Original request for help

Save

Abstract

En 中文

In recent years, with the deep integration of big data and medical technology, hybrid data with or without block-wise missing arise more commonly in medical care. Efficient dimensionality reduction and extraction of important predictive information for such data have also become a popular research topic. In this article, for hybrid data without missing and with block-wise missing, we proposed a kind of new component-based model based on the unified approach to multi-source principal component analysis and multi-set canonical correlation analysis. After obtaining scores by using the unified framework, component-based regression models are established. Asymptotic properties are established under some mild conditions. Simulations and real data analysis show the proposed method works well.

Keywords:

Alzheimer's disease

block-wise imputation

component-based regression

hybrid data

multi-set canonical correlation analysis

multi-source principal component analysis

COMPONENT-BASED REGRESSION FOR HYBRID DATA

Abstract

Journal

Organization