Statistics Colloquium
November 13 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Nonparametric Longitudinal Analysis of Irregularly Observed Noisy Data

Sujit K. Ghosh
North Carolina State University and SAMSI

Analysis of irregularly observed longitudinal data within a mixed model framework often are based on stringent parametric or semiparametric model assumptions that are often too restrictive in practice. In particular, finite dimensional (parametric) models are found inadequate to address the complex relationship between the response and predictors. A majority of the currently available models and associated estimation methodologies are based on restrictive assumptions on the correlation structure of longitudinal data. To begin with, a flexible class of non-parametric models based on a sequence of Bernstein polynomials with varying degrees is developed and the degree of the polynomials is chosen using a newly proposed predictive divergence criteria. Various simulated data scenarios are used to illustrate the superior performance of the proposed estimation and model selection methodology. The newly proposed models and associated inference are illustrated using real data analysis of various growth curves.
[This is a joint work with Dr. Liwei Wang, PPD Inc.]

All statistics students are expected to attend.


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Department of Statistics
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Western Michigan University
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