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regression - Alternative to Cross validation - Cross Validated I have implemented different cross validation algorithms, like CV and GCV, and they are working perfectly I have to run it on an arm processor, and the time of calculation is very high Since the
aic - Huge ΔAIC between GAM and GAMM models - Cross Validated In GAMM there is no need for explicit penalization as in GAMs, hence no GCV UBRE needed to pick the best lambda (penalty coefficient) In GAMMs lambda is part of the variance components and is estimated in the model I suppose to perform a test on the effect of the random variable, you could compare two models
Spline df selection in a general additive Poisson model problem However, GCV is generally a poor choice as it has a tendency to undersmooth See, for example Hurvich et al (1998) "Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion" (Available without subscription here)
Understanding ridge regression results - Cross Validated Anyway, $\lambda = 0$ implies zero penalty, hence the least squares estimates are optimal in the sense that they had the lowest GCV (generalised cross validation) score However, you may not have allowed sufficiently large a penalty; in other words, the least squares estimates were optimal of the small set of of $\lambda$ values you looked at
Maximum penalty for ridge regression - Cross Validated Consider a regression model $$ y = X \\beta + \\varepsilon $$ I will use ridge regression to estimate $\\beta$ Ridge regression contains a tuning parameter (the penalty intensity) $\\lambda$ If I
Find good smoothing spline factor - Cross Validated I'd like an automatic way to find the "best" smoothing factor s for a spline fit to a given set of data points Here's a sample visualization of some data and the fit splines for various s values:
GAM cross-validation to test prediction error The prediction error criteria used are Generalized (Approximate) Cross Validation (GCV or GACV) when the scale parameter is unknown or an Un-Biased Risk Estimator (UBRE) when it is known "