Cross-Validation, the Jackknife, and the Bootstrap: Excess Error Estimation in Forward Logistic Regression
作者:
Gail Gong,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1986)
卷期:
Volume 81,
issue 393
页码: 108-113
ISSN:0162-1459
年代: 1986
DOI:10.1080/01621459.1986.10478245
出版商: Taylor & Francis Group
关键词: Prediction;Error rate estimation;Variables selection
数据来源: Taylor
摘要:
Given a prediction rule based on a set of patients, what is the probability of incorrectly predicting the outcome of a new patient? Call this probability the true error. An optimistic estimate is the apparent error, or the proportion of incorrect predictions on the original set of patients, and it is the goal of this article to study estimates of the excess error, or the difference between the true and apparent errors. I consider three estimates of the excess error: cross-validation, the jackknife, and the bootstrap. Using simulations and real data, the three estimates for a specific prediction rule are compared. When the prediction rule is allowed to be complicated, overfitting becomes a real danger, and excess error estimation becomes important. The prediction rule chosen here is moderately complicated, involving a variable-selection procedure based on forward logistic regression.
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