Validation of Regression Models: Methods and Examples
作者:
RonaldD. Snee,
期刊:
Technometrics
(Taylor Available online 1977)
卷期:
Volume 19,
issue 4
页码: 415-428
ISSN:0040-1706
年代: 1977
DOI:10.1080/00401706.1977.10489581
出版商: Taylor & Francis Group
关键词: Model Validation;Cross Validation;Regression Analysis;Data Splitting;Model Assessment
数据来源: Taylor
摘要:
Methods to determine the validity of regression models include comparison of model predictions and coefficients with theory, collection of new data to check model predictions. comparison of results with theoretical model calculations, and data splitting or cross-validation in which a portion of the data is used toestimatethe model coefficients, and the remainder of the data is used to measure thepredictionaccuracy of the model. An expository review of these methods is presented. It is concluded that data splitting is an effective method of model validation when it is not practical to collect new data to test the model. The DUPLEX algorithm, developed by R. W. Kennard, is recommended for dividing the data into the estimation set and prediction set when there is no obvious variable such as time to use as a basis to split the data. Several examples are included to illustrate the various methods of model validation.
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