{"product_id":"smoothing-techniques-with-implementation-in-s-springer-series-in-statistics-0387973672-used","title":"Smoothing Techniques: With Implementation in S (Springer Series in Statistics)","description":"\u003cp\u003e\u003cstrong\u003eISBN:\u003c\/strong\u003e 0387973672\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor:\u003c\/strong\u003e H\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e Used\u003c\/p\u003e\u003cp\u003eThe author has attempted to present a book that provides a non-technical introduction into the area of non-parametric density and regression function estimation. The application of these methods is discussed in terms of the S computing environment. Smoothing in high dimensions faces the problem of data sparseness. A principal feature of smoothing, the averaging of data points in a prescribed neighborhood, is not really practicable in dimensions greater than three if we have just one hundred data points. Additive models provide a way out of this dilemma; but, for their interactiveness and recursiveness, they require highly effective algorithms. For this purpose, the method of WARPing (Weighted Averaging using Rounded Points) is described in great detail.\u003c\/p\u003e","brand":"Mia Karts","offers":[{"title":"Default Title","offer_id":67836329787680,"sku":"USED0387973672","price":29.0,"currency_code":"CAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0980\/7426\/3840\/files\/61Tgd3xXC3L_395ad772-df88-480d-a3f9-8ea0ec138dbd.jpg?v=1792258239","url":"https:\/\/miakarts.com\/en-ca\/products\/smoothing-techniques-with-implementation-in-s-springer-series-in-statistics-0387973672-used","provider":"Miakarts Books","version":"1.0","type":"link"}