Longitudinal Data Regression Analysis Using Semiparametric Modelling

Document Type

Presentation Abstract

Presentation Date

4-17-2023

Abstract

In the longitudinal data regression model, performing joint analysis of mean and covariance parameters simultaneously and accounting for the correlations improve statistical inference of the mean of interest. Zhang, Leng and Tang (2015) propose joint parametric modelling of the means, variances, and correlations by decomposing the correlation matrix via hyperspherical coordinates. In this presentation, I will talk about the methodology for joint estimation in semiparametric modelling of the means, the variances, and by decomposing the correlation matrix via hyperspherical coordinates. The method is illustrated to analyze one health data set.

Additional Details

April 17, 2023 at 3:00 p.m. Math 103

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