Department seminar by Yong Chen, University of PennsylvaniaExport this event to calendar

Thursday, March 7, 2019 — 4:00 PM EST

Non-standard problems in statistical inference:Bartlett identity, boundary, identifiability issues


In this talk, I will cover a few ideas in tackling non-standard problems in statistical inference, including Bartlett identity, boundary and identifiability issues. I will show that these considerations are critical in model robustness, statistical power, and validity. I will also present implications of these ideas in addressing key challenges in biomedical research using massive healthcare data, in particular, electronic health records, drug/vaccine safety surveillance data. Case studies using University of Pennsylvania Biobank data will be provided.References:

Chen, Y and Liang, KY. (2010). On the asymptotic behaviour of the pseudolikelihood ratio test statistic with boundaryproblems, Biometrika, 97 (3), 603–620.

Ning, Y and Chen, Y (2015) Test for homogeneity in semiparametric exponential tilt mixture models, Scandinavian Journal of Statistics.

Chen, Y, Huang, J, Ning, Y, Liang, K-Y and Lindsay, B (2018) A conditional test for composite likelihood withboundary constraints. Biometrika. 105 (1): 225-232, March 2018.

Chen, Y, Ning, J, Ning, Y, Liang, K-Y and Bandeen-Roche, K (2017) On the pseudolikelihood inference for semiparametric models with boundary problems. Biometrika. 104 (1): 165-179, March 2017.

Hong, C, Ning, Y, Wang, S, Wu, H, Carroll, RJ and Chen, Y (2017) PLEMT: A novel pseudolikelihood based EM testfor homogeneity in generalized exponential tilt mixture models, JASA. 112(520): 1393-1404. 2017.

Duan, R, Ning, Y, Wang, S, Carroll, RJ, and Chen, Y (2018) A score test for heterogeneity in a semiparametric mixture model, Biometrics (under revision).

Huang, J, Ning, Y, Reid, N and Chen, Y (2018) On specification test for composite likelihood, Biometrika (under revision).

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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