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--- seminars:stat:161117 [2016/11/09 20:50] – shang
+++ seminars:stat:161117 [2016/11/17 00:12] (current) – shang
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+~~META:title =November 17, 2016~~
+<WRAP 70% center>
+^  **DATE:**|Thursday, November 17, 2016 |
+^  **TIME:**|1:15pm to 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Ruiqi Liu, Binghamton University |
+^  **TITLE:**|Statistical inference on panel data: a kernel ridge regression approach|
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+Panel Data is common in financial and economical areas. Various models have been developed to explain the relationship between variables. For example, the classic random/fixed effect model can discover the individual effects and common effects among different subjects, while the common factor model is able to characterize the cross-sectional factors with different individual factor loading. In our work, a non-parametric interactive fixed effects (NIFE) model, unifying the existing popular models, is considered which includes a heterogeneous or homogeneous non-parametric component, an unobservable cross-sectional factor and unobservable factor loading. We propose a kernel ridge regression approach to estimate the non-parametric function and model parameters. Convergence rate and asymptotic normality are established in both heterogeneous and homogeneous cases. Numerical evidence supports our results.
+</WRAP>