{"product_id":"9789814596084","title":"The Fence Methods","description":"\u003cp\u003eThis book is about a recently developed class of strategies, known as the fence methods, which fits particularly well in non-conventional and complex model selection problems with practical considerations. The idea involves a procedure to isolate a subgroup of what are known as correct models, of which the optimal model is a member. This is accomplished by constructing a statistical \u003ci\u003efence\u003c\/i\u003e, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from amongst those within the fence according to a criterion which can be made flexible. In particular, the criterion of optimality can incorporate consideration of practical interest, thus making model selection a real life practice.\u003c\/p\u003e\u003cp\u003eFurthermore, this book introduces a data-driven approach, called \u003ci\u003eadaptive fence\u003c\/i\u003e, which can be used in a wide range of problems involving determination of tuning parameters, or constants. Instead of relying on asymptotic theory, the fence focuses on finite-sample performance, and computation. Such features are particularly suitable to statistics in the new era.\u003c\/p\u003e\u003cp\u003eThis book is about a recently developed class of strategies, known as the fence methods, which fits particularly well in non-conventional and complex model selection problems with practical considerations. The idea involves a procedure to isolate a subgroup of what are known as correct models, of which the optimal model is a member. This is accomplished by constructing a statistical \u003ci\u003efence\u003c\/i\u003e, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from amongst those within the fence according to a criterion which can be made flexible. In particular, the criterion of optimality can incorporate consideration of practical interest, thus making model selection a real life practice.\u003c\/p\u003e\u003cp\u003eFurthermore, this book introduces a data-driven approach, called \u003ci\u003eadaptive fence\u003c\/i\u003e, which can be used in a wide range of problems involving determination of tuning parameters, or constants. Instead of relying on asymptotic theory, the fence focuses on finite-sample performance, and computation. Such features are particularly suitable to statistics in the new era.\u003c\/p\u003e\u003cb\u003eReadership:\u003c\/b\u003e Graduates and researchers interested in a new class of strategies for model selection.\u003cbr\u003e\u003cb\u003eKey Features:\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eIntroduces a general data-driven procedure, and let the data speak in choosing some critical tuning constants\u003c\/li\u003e\n\u003cli\u003eTargets non-conventional and complex problems, and focuses on finite-sample performance and computation\u003c\/li\u003e\n\u003cli\u003eMakes model selection a real life practice\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"World Scientific Publishing Company, Incorporated","offers":[{"title":"Default Title","offer_id":47150209073392,"sku":"9789814596084","price":38.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9789814596084_p0.jpg?v=1763691263","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9789814596084","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}