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- Title
On Testing an Unspecified Function Through a Linear Mixed Effects Model with Multiple Variance Components.
- Authors
Wang, Yuanjia; Chen, Huaihou
- Abstract
We examine a generalized F-test of a nonparametric function through penalized splines and a linear mixed effects model representation. With a mixed effects model representation of penalized splines, we imbed the test of an unspecified function into a test of some fixed effects and a variance component in a linear mixed effects model with nuisance variance components under the null. The procedure can be used to test a nonparametric function or varying-coefficient with clustered data, compare two spline functions, test the significance of an unspecified function in an additive model with multiple components, and test a row or a column effect in a two-way analysis of variance model. Through a spectral decomposition of the residual sum of squares, we provide a fast algorithm for computing the null distribution of the test, which significantly improves the computational efficiency over bootstrap. The spectral representation reveals a connection between the likelihood ratio test (LRT) in a multiple variance components model and a single component model. We examine our methods through simulations, where we show that the power of the generalized F-test may be higher than the LRT, depending on the hypothesis of interest and the true model under the alternative. We apply these methods to compute the genome-wide critical value and p-value of a genetic association test in a genome-wide association study (GWAS), where the usual bootstrap is computationally intensive (up to 108 simulations) and asymptotic approximation may be unreliable and conservative.
- Subjects
F-test (Mathematical statistics); SPLINES; NONPARAMETRIC estimation; GENOMES; ANALYSIS of variance; LIKELIHOOD ratio tests
- Publication
Biometrics, 2012, Vol 68, Issue 4, p1113
- ISSN
0006-341X
- Publication type
Article
- DOI
10.1111/j.1541-0420.2012.01790.x