Diagnostics for Nonparametric Regression Models with Additive Terms
作者:
Chong Gu,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 420
页码: 1051-1058
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10476260
出版商: Taylor & Francis Group
关键词: Concurvity;Cosine diagnostics;Interaction splines;Model redundancy;Retrospective analysis
数据来源: Taylor
摘要:
Recent developments of multivariate smoothing methods provide a rich collection of feasible models for nonparametric multivariate data analysis. Among the most interpretable are models with additive terms. Construction of various models and algorithms for computing the models have been the main concern of the existing literature in this area. Few results are available on the validation of computed fits, and many applications of nonparametric methods unfortunately end up interpreting the noise. This article proposes and illustrates some simple retrospective diagnostics to help data analysts in detecting possible aliasing effects in computed nonparametric fits and in building parsimonious models in an interactive fashion. It also discusses the concepts and rationale behind the proposal, including concurvity, diagnostics versus tests, and so forth. For their ready availability, interaction splines are used in the illustrations.
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