A note on estimating the msep in nonlinear regression
From MaRDI portal
Publication:3776421
DOI10.1080/02331888708802047zbMath0636.62067OpenAlexW2113347646MaRDI QIDQ3776421
Publication date: 1987
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888708802047
cross-validationbootstrap estimatesmean squared error of predictioninadequate modelselection of regression models
Linear regression; mixed models (62J05) Nonparametric estimation (62G05) General nonlinear regression (62J02)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Bootstrap and cross-validation estimates of the prediction error for linear regression models
- Asymptotic properties of criteria for selection of variables in multiple regression
- Asymptotic mean efficiency of a selection of regression variables
- Estimating the dimension of a model
- Bootstrap methods: another look at the jackknife
- Statistical predictor identification
- Estimators of the Mean Squared Error of Prediction in Linear Regression
- Approximate efficiency of a selection procedure for the number of regression variables
- Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
- Statistische Methoden der Modellbildung, II
- A third order asymptotic comparison of least squares, jack-knifing and cross-validation for error variance estimation in nonlinear regression
- A stepwise procedure for the selection of nonlinear regression models
- Jackknifing in Nonlinear Regression
- A note on the generalized information criterion for choice of a model
- An optimal selection of regression variables
- A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
- Comparison of Stopping Rules in Forward "Stepwise" Regression
- Some Comments on C P
- A new look at the statistical model identification
This page was built for publication: A note on estimating the msep in nonlinear regression
