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Full-Text Articles in Multivariate Analysis
Nonparametric Tests Of Lack Of Fit For Multivariate Data, Yan Xu
Nonparametric Tests Of Lack Of Fit For Multivariate Data, Yan Xu
Theses and Dissertations--Statistics
A common problem in regression analysis (linear or nonlinear) is assessing the lack-of-fit. Existing methods make parametric or semi-parametric assumptions to model the conditional mean or covariance matrices. In this dissertation, we propose fully nonparametric methods that make only additive error assumptions. Our nonparametric approach relies on ideas from nonparametric smoothing to reduce the test of association (lack-of-fit) problem into a nonparametric multivariate analysis of variance. A major problem that arises in this approach is that the key assumptions of independence and constant covariance matrix among the groups will be violated. As a result, the standard asymptotic theory is not …
Nonparametric Analysis Of Clustered And Multivariate Data, Yue Cui
Nonparametric Analysis Of Clustered And Multivariate Data, Yue Cui
Theses and Dissertations--Statistics
In this dissertation, we investigate three distinct but interrelated problems for nonparametric analysis of clustered data and multivariate data in pre-post factorial design.
In the first project, we propose a nonparametric approach for one-sample clustered data in pre-post intervention design. In particular, we consider the situation where for some clusters all members are only observed at either pre or post intervention but not both. This type of clustered data is referred to us as partially complete clustered data. Unlike most of its parametric counterparts, we do not assume specific models for data distributions, intra-cluster dependence structure or variability, in effect …