seminars:stat:210506 [Department of Mathematics and Statistics, Binghamton University]

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.

<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>

<WRAP 70% center>
^  **DATE:**|Thursday, May 6, 2021 |
^  **TIME:**|1:15pm -- 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Yifeng Zheng, Binghamton University |
^  **TITLE:**|A Penalized Spline Approach to Functional Mixed Effects Model Analysis  |
</WRAP>
\\ 

<WRAP center box 80%>
<WRAP centeralign>**Abstract**</WRAP>
In this
article of  Huaihou Chen and Yuanjia Wang, they proposed penalized spline (P-spline)-based methods for
functional mixed effects models with varying coefficients. They decomposed
longitudinal outcomes as a sum of several terms: a population mean
function, covariateswith time-varying coefficients, functional
subject-specific random effects, and residual measurement error processes.
Proposed methods offer flexible estimation of both the population- and
subject-level curves. In addition, decomposing variability of the outcomes
as a between- and within-subject source is useful in identifying the
dominant variance component therefore optimally model a covariance
function.The benefit of the between- and within-subject covariance
decomposition is illustrated through an analysis of Berkeley growth data,
where they identified clearly distinct patterns of the between- and
within-subject covariance functions of children’s heights.
</WRAP>