Functional measurement error in functional regression

WAKE FOREST UNIVERSITY
DEPARTMENT OF MATHEMATICS & STATISTICS

Presents

Sneha Jadhav
Wake Forest University

Tuesday, November 7th, 2019
11am
Carswell Hall, Room 101

Abstract: Measurement error is an important problem that has not been very well studied in the context of
Functional Data Analysis. To the best of our knowledge, there are no existing methods that address the
presence of functional measurement errors in generalized functional linear models. We propose a novel
approach for estimating the slope function in the presence of measurement error in the generalized
functional linear model with a scalar response. This work significantly advances the existing conditional-
score method to accommodate the case where both the measurement error and independent variables
lie in infinite dimensional spaces. Asymptotic results are established for the proposed estimate, and its
behavior is studied via simulations, where the response is continuous or binary. Analysis of the Canadian
Weather data highlights the practical utility of our method.

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