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Activity Number:
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76
- Recent Advances in Multivariate Analysis for Modern Scientific Studies and Application
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Type:
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Topic-Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Korean International Statistical Society
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Abstract #317172
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Title:
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Envelope-Based Partial Least Squares with Application to Cytokine-Based Biomarker Analysis for COVID-19
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Author(s):
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Yeonhee Park* and Zhihua Su and Dongjun Chung
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Companies:
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University of Wisconsin and University of Florida and Ohio State University
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Keywords:
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Dimension reduction;
Envelope model;
Grassmann manifold;
Multivariate regression;
Partial least squares
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Abstract:
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Partial least squares (PLS) regression is a popular alternative to ordinary least squares (OLS) regression because of its superior prediction performance demonstrated in many cases. In various contemporary applications, the predictors include both continuous and categorical variables. A common practice in PLS regression is to treat the categorical variable as continuous. However, studies find that this practice may lead to biased estimates and invalid inferences (Schuberth et al. 2018). In this paper, we develop an envelope-based partial PLS that considers the PLS regression on the conditional distributions of the response(s) and continuous predictors on the categorical predictors. Numerical study shows that this approach can achieve more efficiency gains in estimation and produce better predictions. The method is applied for the identification of cytokine-based biomarkers for COVID-19 patients, which reveals the association between the cytokine-based biomarkers and patients' clinical information including disease status at admission and demographical characteristics.
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Authors who are presenting talks have a * after their name.
