Local and omnibus goodness-of-fit tests in classical measurement error models
Type
ArticleKAUST Grant Number
KUS-C1-016-04Date
2010-09-14Online Publication Date
2010-09-14Print Publication Date
2011-01Permanent link to this record
http://hdl.handle.net/10754/598222
Metadata
Show full item recordAbstract
We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and computational advantages that are similar to those of the classical score tests in the parametric model framework. The test procedures are applicable to several semiparametric extensions of measurement error models, including when the measurement error distribution is estimated non-parametrically as well as for generalized partially linear models. The performance of the local score-type and omnibus goodness-of-fit tests is demonstrated through simulation studies and analysis of a nutrition data set.Citation
Ma Y, Hart JD, Janicki R, Carroll RJ (2010) Local and omnibus goodness-of-fit tests in classical measurement error models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73: 81–98. Available: http://dx.doi.org/10.1111/j.1467-9868.2010.00751.x.Sponsors
Carroll and Ma's research was supported by a grant from the National Cancer Institute (CA57030). Carroll and Hart's work was also supported by award KUS-C1-016-04, made by King Abdullah University of Science and Technology. Ma's work is supported by National Science Foundation grant DMS-0906341, and Hart's was partially supported by National Science Foundation grant DMS-0604801. Janicki's work was done while at the University of Maryland. He thanks his adviser Professor Abram Kagan for his advice and support.Publisher
WileyPubMed ID
21339886PubMed Central ID
PMC3040518ae974a485f413a2113503eed53cd6c53
10.1111/j.1467-9868.2010.00751.x
Scopus Count
Collections
Publications Acknowledging KAUST SupportRelated articles
- A hybrid omnibus test for generalized semiparametric single-index models with high-dimensional covariate sets.
- Authors: Xu Y, Kim I, Carroll RJ
- Issue date: 2019 Sep
- Estimation via corrected scores in general semiparametric regression models with error-prone covariates.
- Authors: Maity A, Apanasovich TV
- Issue date: 2011
- A semiparametric copula method for Cox models with covariate measurement error.
- Authors: Kim S, Li Y, Spiegelman D
- Issue date: 2016 Jan
- Variable Selection in Measurement Error Models.
- Authors: Ma Y, Li R
- Issue date: 2010
- A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.
- Authors: Schörgendorfer A, Branscum AJ, Hanson TE
- Issue date: 2013 Jun