##Statistics Seminar##\\ Department of Mathematical Sciences
^ **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 |
\\
**Abstract**
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.