{"product_id":"sparse-ridge-fusion-for-linear-regression-von-nozad-h-mahmood","title":"Sparse Ridge Fusion For Linear Regression","description":"\u003cp\u003eFor a linear regression, the traditional technique deals with a case where the number of observations n are more than the number of predictors variables p (n\u0026gt;p). In the case n\u003c\/p\u003e\u003cp the classical method fails to estimate coefficients. a solution of this problem in case correlated predictors is provided book. new regularization and variable selection proposed under name sparse ridge fusion highly predictor effect simulated examples real data show that srf always outperforms than lasso elastic net s-lasso also results selects more variables sample size n while maximum selected by size.\u003e\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783659750946\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9783659750946","offer_id":39449489735773,"sku":"9783659750946","price":39.9,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/7212763c-e599-4544-b630-f7c36f556a28.jpg?v=1773469269","url":"https:\/\/shop.autorenwelt.de\/en\/products\/sparse-ridge-fusion-for-linear-regression-von-nozad-h-mahmood","provider":"Autorenwelt Shop","version":"1.0","type":"link"}