{"product_id":"measurement-error-models-methods-and-applications-9781032477688","title":"Measurement Error: Models, Methods, and Applications","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eMeasurement Error: Models, Methods, and Applications provides an overview of the main techniques for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters. It emphasizes the use of several simple methods, such as moment corrections, regression calibration, SIMEX, modified estimating equation methods, and likelihood techniques, and uses SAS-IML and Stata to implement them. The book is accessible to a broad audience and describes basic models and methods, their uses in a range of application areas, and the associated terminology. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 464 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 21 January 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eOver the past two decades, comprehensive strategies have emerged for addressing measurement error in complex models and accounting for the utilization of additional data to estimate measurement error parameters. This comprehensive guide, Measurement Error: Models, Methods, and Applications, delves into both established and novel approaches, offering a comprehensive overview of the primary techniques and showcasing their practical applications across a diverse range of models. It sheds light on the consequences of disregarding measurement errors in naive analyses and presents effective methods for correcting them across various statistical models, spanning from simple one-sample problems to regression models and even more intricate mixed and time series models.\u003cbr\u003e\u003cbr\u003eThe book comprehensively covers correction methods based on known measurement error parameters, replication, internal or external validation data, and, in certain cases, instrumental variables. It emphasizes the use of several relatively straightforward approaches, including moment corrections, regression calibration, simulation extrapolation (SIMEX), modified estimating equation methods, and likelihood techniques. The author employs SAS-IML and Stata to implement numerous techniques in the examples provided throughout the book.\u003cbr\u003e\u003cbr\u003eDesigned to be accessible to a broad audience, this book elucidates the process of modeling measurement error, the implications of ignoring it, and the strategies for correcting it. It serves as a valuable resource for individuals seeking to gain a deeper understanding of measurement error and its applications in diverse fields. With its emphasis on basic models and methods, their practicalusages in various application areas, and the associated terminology, Measurement Error: Models, Methods, and Applications provides a comprehensive and practical guide for researchers, practitioners, and students alike.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 860g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 234 x 156 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781032477688\u003c\/p\u003e","brand":"John P. Buonaccorsi","offers":[{"title":"Paperback \/ softback","offer_id":44104704491770,"sku":"9781032477688","price":46.64,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_23c42af1-0b72-4de6-93d1-8174059c22fb.jpg?v=1675330793","url":"https:\/\/shulphink.com\/products\/measurement-error-models-methods-and-applications-9781032477688","provider":"Shulph Ink","version":"1.0","type":"link"}