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The word shrinkage gets thrown around a lot in certain circles. But what is shrinkage, there does not seem to be a clear definition. … What are the different types of theoretical shrinkage I can talk about? How can shrinkage help in prediction? Can people provide some good insight or references? …

I am building a Bayesian model where I to put shrinkage priors such as spike and slab, horseshoe prior, etc on some parameters for feature selection, but I am not able to decide which one is the best. …

I am currently reviewing the below paper and was wondering if it was possible to correctly implement the BIC equation for "BIC-LASSO Shrinkage". … I've been reviewing the below function ic.glmnet but just wanted to make sure the formula used here is correct for BIC-LASSO Shrinkage. …

I'm unclear how the shrinkage parameter works in Adaboost. … I understand the concept of shrinkage in the theoretical sense related to ordinary least squares, but I'm not sure how to interpret this parameter in relation to Adaboost. …

I have a question regarding Shrinkage Methods. … In it they used one formula for the total cost of a regression, which seems to justify using shrinkage best: Total Cost = measure of fit + measure of magnitude of coefficients However, the formula …

), and $F$ is the shrinkage target. … In shrinkage, the most challenging part is how to compute (automatically) the shrinkage intensity. …

LASSO methods are used in estimating predictive models, and they rely on "shrinkage" of coefficient estimates for model selection. … When I first saw the LASSO method ("Least Absolute Shrinkage...") I thought it was a short cut to a better predictor. …

I have the following question regarding the Proof of "Shrinkage" in Statistics. … Up to the present moment, I always thought that "Shrinkage" was a synonymous term with "Regularization", and referred to a technique in statistics used to combat the "Bias-Variance Tradeoff" within predictive …

As in, for example in "The Risk of James–Stein and Lasso Shrinkage" by Hansen or "Ridge regression and James-Stein estimation - review and comments" by Draper et al. … So I would like to calculate shrinkage coefficient for each $b_k$ using information about coefficients from other univariate regressions. …

How is that shrinkage better than / different from fixed-effects logistic regression with one-hot-encoding of ID variables and shrinkage via L2-regularization? … In a fixed-effects model, I can control the amount of shrinkage by changing my penalty, $\lambda$, of L2-regularization while in a random-effects model I have no control on the amount of shrinkage. …

Where do I see the shrinkage? Or is there no shrinkage going on? …

I am trying to understand why the covariance estimation shrinkage works. Is there any theory behind any of these approach? …

When estimating multivariate means, do all shrinkage approaches preserve partial order? I mean do the order of the means from a MLE estimate change after shrinkage? … Are there any approaches to shrinkage that doesn't preserve partial order (basically making them equal after shrinkage doesn't count, such as hard thresholding). …

I am wondering — is there any rule of thumb on the dispersion (or shrinkage parameter in Python imbalanced learn (imblearn) package? …

I have been using linear shrinkage to better estimate the covariance matrix when I do not have enough data. … Now, let $\bf \hat\Sigma$ be the covariance estimated by linear shrinkage (proposed by Ledoit-Wolf). …