Journal article
Checking linearity of non-parametric component in partially linear models with an application in systemic inflammatory response syndrome study
Statistical methods in medical research, v 15(3), pp 273-284
Jun 2006
PMID: 16768300
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Two tests are proposed for checking the linearity of nonparametric function in partially linear models. The first one is based on a Crámer-von Mises statistic. This test can detect the local alternative converging to the null at the parametric rate 1/square root n. A bootstrap resample technique is provided to calculate the critical values. The second one is constructed in a penalized spline framework along with linear mixed-effects (LME) modeling. This is an extension likelihood ratio test for testing zero variance of random effects in LME models. Simulation experiments are conducted to explore the numerical performance of two tests. It is observed that two tests have good level properties, and the first test has a substantially superior power property over the second test in a variety of cases. A real data set is analysed with the proposed tests.
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Details
- Title
- Checking linearity of non-parametric component in partially linear models with an application in systemic inflammatory response syndrome study
- Creators
- Hua Liang - University of Rochester Medical CenterHualou Liang - School of Biomedical Engineering, Science, and Health Systems (1997-)
- Publication Details
- Statistical methods in medical research, v 15(3), pp 273-284
- Grant note
- AI50020 / NIAID NIH HHS AI59773 / NIAID NIH HHS AI62247 / NIAID NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000237575700004
- Scopus ID
- 2-s2.0-33744817342
- Other Identifier
- 991019320608804721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Health Care Sciences & Services
- Mathematical & Computational Biology
- Medical Informatics
- Statistics & Probability