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--- seminars:stat:201112 [2020/11/03 17:57] – created qyu
+++ seminars:stat:201112 [2020/11/03 19:44] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, Nov. 12, 2020 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Xiaoke Qin, Binghamton University |
+^  **TITLE:**|Variable selection for sparse Dirichlet-Multinomail regression with an application to microbiome data analysis. |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+With the development of next generation sequencing technology,
+researchers have now been able to study the microbiome composition using
+direct sequencing, whose output are bacterial taxa counts for each
+microbiomesample. One goal of microbiome study is to associate the
+microbiome composition with environmental covariates. This paper
+proposes to model the taxa counts using a Dirichlet-multinomial (DM)
+regression model in order to account for overdispersion of observed
+counts. The DM regression model can be used for testing the association
+between taxa composition and covariates using the likelihood ratio test.
+However, when the number of covariates is large, multiple testing can
+lead to loss of power. To address the high dimensionality of the
+problem, a penalized likelihood approach is proposed to estimate the
+regression parameters and to select the variables by imposing a sparse
+group l_2 penalty to encourage both group-level and within-group
+sparsity. Such a variable selection procedure can lead to selection of
+the relevant covariates and their associated bacterial taxa. An
+efficient block-coordinate descent algorithm is developed to solve the
+optimization problem in this paper. The authors also demonstrate the
+power of the method in the analysis of a data set evaluating the
+nutrient intake on the human gut microbiome.
+</WRAP>