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--- seminars:stat:210506 [2021/04/29 12:13] – created qyu
+++ seminars:stat:210506 [2021/04/29 12:17] (current) – qyu
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+<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>