Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models

Marina Bogomolov, Ori Davidov

Research output: Contribution to journalArticlepeer-review

Abstract

In a variety of applications researchers are interested in comparing two or more naturally ordered experimental conditions after adjusting for covariates. Addressing this problem we develop a methodology for estimating a mean response conditional on covariates in the framework of partially linear models which allows the effects of some covariates to be modeled nonparametrically. Our focus is on univariate responses but extensions to multivariate response data are also considered. The new methodology is applied to data from a study that examined the relationship between exposure to PFASs, a class of widely used environmental pollutants, and plasma lipids in a cohort of pregnant women.

Original languageEnglish
Pages (from-to)20-27
Number of pages8
JournalComputational Statistics and Data Analysis
Volume133
DOIs
StatePublished - May 2019

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.

Keywords

  • Analysis of covariance
  • Order restricted statistical inference
  • Partially linear model

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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