|
Activity Number:
|
146
- Functional and High-Dimensional Analysis
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 8, 2022 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
ENAR
|
|
Abstract #323683
|
|
|
Title:
|
Generalized Functional Linear Regression Model with Functional and Scalar Covariates Measured with Measurement Error
|
|
Author(s):
|
Yuanyuan Luan* and Roger S. Zoh and Carmen D. Tekwe
|
|
Companies:
|
Indiana University and Indiana University and Indiana University
|
|
Keywords:
|
Accelerometers;
type 2 diabetes;
dietary intake;
functional data;
measurement error;
NHANES
|
|
Abstract:
|
While extensive work has been done to correct for biases due to measurement error in scalar-valued covariates prone to errors in generalized linear regression models. Limited work has been done to address biases associated with functional covariates prone to errors or the combination of scalar and functional covariates prone to errors in generalized linear regression models. In this work, we propose semiparametric and parametric approaches to correct for measurement errors associated with a mixture of functional and scalar covariates prone to errors in generalized linear regression. The developed methods are applied to investigate the influence of wearable-device-based physical activity and self-reported measures of dietary intake on the probability of type 2 diabetes diagnosis. We treat the device-based measures of physical activity as error prone functional covariates prone to complex arbitrary heteroscedastic errors, while dietary intake is considered a scalar-valued covariate prone to error. We present simulation studies to assess the finite sample properties of our proposed methods. The developed methods are applied to National Health and Examination Survey data to assess the
|
Authors who are presenting talks have a * after their name.
