NONPARAMETRIC ANALYSIS OF COVARIANCE

被引:102
|
作者
YOUNG, SG
BOWMAN, AW
机构
[1] Department of Statistics, University of Glasgow
关键词
ANALYSIS OF COVARIANCE; BIAS; DROPOUTS; GASSER-MULLER ESTIMATOR; LONGITUDINAL DATA; MISSING DATA; MOMENTS; NONLINEAR REGRESSION; PARALLELISM; QUADRATIC FORM; SEMIPARAMETRIC;
D O I
10.2307/2532993
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
An analysis of covariance model where the covariate effect is assumed only to be smooth is considered. The possibility of different shapes of covariate effect in different groups is also allowed and tests of equality and of parallelism across groups are constructed. These are implemented using Gasser-Muller smoothing, whose properties enable problems of bias to be avoided. Accurate moment-based approximations are available for the distribution of each test statistic. Some data on Spanish Onions are used to contrast the non-parametric approach with that of a nonlinear, but parametric, model. A simulation study is also used to explore the properties of the non-parametric tests.
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页码:920 / 931
页数:12
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