Parametric mode regression for bounded responses

Author ORCID Identifier

Haiming Zhou:https://orcid.org/0000-0002-2777-5354

Publication Title

Biometrical Journal

ISSN

03233847

E-ISSN

44004

Document Type

Article

Abstract

We propose new parametric frameworks of regression analysis with the conditional mode of a bounded response as the focal point of interest. Covariate effects estimation and prediction based on the maximum likelihood method under two new classes of regression models are demonstrated. We also develop graphical and numerical diagnostic tools to detect various sources of model misspecification. Predictions based on different central tendency measures inferred using various regression models are compared using synthetic data in simulations. Finally, we conduct regression analysis for data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate practical implementation of the proposed methods. Supporting Information that contain technical details and additional simulation and data analysis results are available online.

First Page

1791

Last Page

1809

Publication Date

11-1-2020

DOI

10.1002/bimj.202000039

PubMed ID

32567136

Keywords

beta distribution, generalized biparabolic distribution, linear predictor, link function, maximum likelihood

Department

Department of Statistics and Actuarial Science

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