{"product_id":"9780471208273","title":"Constrained Statistical Inference Order, Inequality, and Shape Constraints","description":"\u003ch3\u003eWiley Series in Probability and Statistics\u003c\/h3\u003e\u003ch1\u003eConstrained Statistical Inference\u003c\/h1\u003e\u003ch2\u003eOrder, Inequality, and Shape Constraints\u003c\/h2\u003e\u003ch3\u003eMervyn J. Silvapulle | Pranab Kumar Sen\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003eAn up-to-date approach to understanding statistical inference  \u003cp\u003eStatistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas.\u003c\/p\u003e \u003cp\u003eConstrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics.\u003c\/p\u003e \u003cp\u003eThe authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions.\u003c\/p\u003e \u003cp\u003eChapter coverage includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePopulation means and isotonic regression\u003c\/li\u003e \u003cli\u003eInequality-constrained tests on normal means\u003c\/li\u003e \u003cli\u003eTests in general parametric models\u003c\/li\u003e \u003cli\u003eLikelihood and alternatives\u003c\/li\u003e \u003cli\u003eAnalysis of categorical data\u003c\/li\u003e \u003cli\u003eInference on monotone density function, unimodal density function, shape constraints, and DMRL functions\u003c\/li\u003e \u003cli\u003eBayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory\u003c\/li\u003e \u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e  MERVYN J. SILVAPULLE, PhD, is an Associate Professor in the Department of Statistical Science at La Trobe University in Bundoora, Australia. He received his PhD in statistics from the Australian National University in 1981.  \u003cp\u003ePRANAB K. SEN, PhD, is a Professor in the Departments of Biostatistics and Statistics and Operations Research at the University of North Carolina at Chapel Hill. He received his PhD in 1962 from Calcutta University, India.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e08 November 2004\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-Interscience\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9780471208273\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e560\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e31.78\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44314341015692,"sku":"9780471208273","price":176.36,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9780471208273.jpg?v=1780176489","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9780471208273","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}