Pages that link to "Item:Q5624520"

The following pages link to Mean Square Error of Prediction as a Criterion for Selecting Variables (Q5624520):

Displaying 40 items.

• Improved Liu estimator in a linear regression model (Q151048) (← links)
• SURE-type functionals as criteria for parametric PSF estimation (Q268705) (← links)
• Akaike-type criteria and the reliability of inference: model selection versus statistical model specification (Q736670) (← links)
• A variable selection procedure for econometric models (Q760747) (← links)
• A note on collinearity, bootstrapping, and cross-validation (Q900042) (← links)
• Prediction error criterion for selecting variables in a linear regression model (Q907107) (← links)
• An alternate version of the conceptual predictive statistic based on a symmetrized discrepancy measure (Q989266) (← links)
• Algorithms for the optimal identification of segment neighborhoods (Q1111966) (← links)
• Dynamics of single-species population growth: Experimental and statistical analysis (Q1154408) (← links)
• A robust method of estimation based on the MML estimators for a simple linear regression model (Q1193993) (← links)
• Feature definition in pattern recognition with small sample size (Q1247178) (← links)
• Generation of CFD meshes from biplane angiograms: an example of image-based mesh generation and simulation. (Q1405142) (← links)
• PRESS model selection in repeated measures data. (Q1606462) (← links)
• A family of the information criteria using the phi-divergence for categorical data (Q1662860) (← links)
• Mean squared error of empirical predictor. (Q1879956) (← links)
• Generalizing the prediction sum of squares statistic and formula, application to linear fractional image warp and surface fitting (Q2193533) (← links)
• Risk reduction for nonlinear prediction and its application to the surrogate data test (Q2448676) (← links)
• A generalization of the growth curve model which allows missing data (Q2558081) (← links)
• Study of a factorial ANOVA model as a generalized linear model (Q2750808) (← links)
• Using cross-validation in principal component analysis (Q2750812) (← links)
• Penalized Partial Likelihood Regression for Right-Censored Data with Bootstrap Selection of the Penalty Parameter (Q3079024) (← links)
• ON THE SELECTION OF SUBSET AUTOREGRESSIVE TIME SERIES MODELS (Q3341715) (← links)
• A Modified winsorized regression procedure for linear models (Q3350538) (← links)
• Sobre la robustificacion interna del algoritmo de Plackett-Kalman para la estimacion recursiva del modelo de regresion lineal (Q3354976) (← links)
• Making robust the cross-validatory choice of smoothing parameter in spline smoothing regression (Q3473171) (← links)
• Model choice for prediction in generalized linear models (Q3492677) (← links)
• Prediction under uncertainty of degree of polynomial in growth curve models (Q3750831) (← links)
• Estimation of general linear-bilinear models for two-way tables (Q4349750) (← links)
• Influential subsets on the variable selection (Q4550637) (← links)
• Asymptotic biases of information and cross-validation criteria under canonical parametrization (Q5078294) (← links)
• General linear estimators under the prediction error sum of squares criterion in a linear regression model (Q5127042) (← links)
• Improved ridge estimators in a linear regression model (Q5128900) (← links)
• (Q5263258) (← links)
• Selection of dimension and basis for density estimation and selection of dimension, basis and error distribution for regression (Q5283842) (← links)
• A Prediction-Oriented Criterion for Choosing the Biasing Parameter in Liu Estimation (Q5421543) (← links)
• Stochastic complexity and the mdl principle (Q5750090) (← links)
• Model selection using PRESS statistic (Q6104415) (← links)
• Properties and parameter estimation of the partly-exponential distribution (Q6180954) (← links)
• Forbidden Knowledge and Specialized Training: A Versatile Solution for the Two Main Sources of Overfitting in Linear Regression (Q6562770) (← links)
• Information criteria for model selection (Q6602021) (← links)