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Activity Number: 418 - SPEED: Biostatistical Methods, Application, and Education, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 2:45 PM
Sponsor: Section on Statistical Graphics
Abstract #307825
Title: Rank-Based Approach for Estimating Correlations in Mixed Ordinal Data
Author(s): Xiaoyun Quan* and James Booth and Martin Wells
Companies: and Cornell University and Cornell University
Keywords: Gaussian copula model; mixed data; sparse modeling; Kendall’s tau; ordinal data; graphical models

High-dimensional mixed data as a combination of both continuous and ordinal variables are widely seen in many research areas such as genomic studies and survey data analysis. Estimating the underlying correlation among mixed data is hence crucial for further inferring dependence structure. We propose a semi-parametric latent Gaussian copula model for this problem. We start with estimating the association among ternary-continuous mixed data via a rank-based approach and generalize the methodology to p-level-ordinal and continuous mixed data. Concentration rate of the estimator is also provided and proved. At last, we demonstrate the performance of the proposed estimator by extensive simulations and two case studies of real data examples of algorithmic risk score evaluation and cancer patients survival data.

Authors who are presenting talks have a * after their name.

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