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--- seminars:stat:210304 [2021/02/08 23:59] – created qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, March 4, 2021 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Jimeng Loh, New Jersey Institute of Technology |
+^  **TITLE:**|Spatial Sampling Design using the Generalized Neyman-Scott Process  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+In this paper we introduce a new procedure for spatial sampling design.
+Previous studies (Zhu and Stein, 2006) have shown that the optimal sampling
+design for spatial prediction with estimated parameters is nearly regular
+with a few clustered points. The pattern is similar to a generalization of
+the Neyman-Scott (GNS) process (Yau and Loh, 2012) which allows for
+regularity in the parent process. This motivates the use of a realization
+of the GNS process as sampling design points. This method translates the
+high dimensional optimization problem of selecting sampling sites into a
+low dimensional optimization problem of searching for the optimal parameter
+sets in the GNS process. Simulation studies indicate that the proposed
+sampling design algorithm is more computationally efficient than
+traditional methods while achieving similar minimization of the criteria
+functions. While the traditional methods become computationally infeasible
+for sample size larger than a hundred, the proposed algorithm is applicable
+to a size as large as n = 1024. A real data example of finding the optimal
+spatial design for
+predicting sea surface temperature in the Pacific Ocean is also considered.
+This is joint work with Szehim Leung, Chunyip Yau and Zhengyuan Zhu.
+</WRAP>