Flexible Locally Weighted Penalized Regression With Applications on Prediction of Alzheimer's Disease Neuroimaging Initiative's Clinical Scores.

ABSTRACT: In recent years, we have witnessed the explosion of large-scale data in various fields. Classical statistical methodologies, such as linear regression or generalized linear regression, often show inadequate performance on heterogeneous data because the key homogeneity assumption fails. In this paper, we present a flexible framework to handle heterogeneous populations that can be naturally grouped into several ordered subtypes. A local model technique utilizing ordinal class labels during the training stage is proposed. We define a new "progression score" that captures the progression of ordinal classes, and use a truncated Gaussian kernel to construct the weight function in a local regression framework. Furthermore, given the weights, we apply sparse shrinkage on the local fitting to handl

SUBMITTER: Wang P 

PROVIDER: S-EPMC7388691 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature