{"product_id":"robust-methods-in-biostatistics-isbn-9780470027264","title":"Robust Methods in Biostatistics","description":"Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers.  \u003cp\u003e\u003ci\u003eRobust Methods in Biostatistics\u003c\/i\u003e proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eLinear regression\u003c\/li\u003e \u003cli\u003eGeneralized linear models\u003c\/li\u003e \u003cli\u003eLinear mixed models\u003c\/li\u003e \u003cli\u003eMarginal longitudinal data models\u003c\/li\u003e \u003cli\u003eCox survival analysis model\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.\u003c\/p\u003e  \u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAcknowledgments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What is Robust Statistics?\u003c\/p\u003e \u003cp\u003e1.2 Against What is Robust Statistics Robust?\u003c\/p\u003e \u003cp\u003e1.3 Are Diagnostic Methods an Alternative to Robust Statistics?\u003c\/p\u003e \u003cp\u003e1.4 How do Robust Statistics Compare with Other Statistical Procedures in Practice?\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Key Measures and Results.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Statistical Tools for Measuring Robustness Properties.\u003c\/p\u003e \u003cp\u003e2.3 General Approaches for Robust Estimation.\u003c\/p\u003e \u003cp\u003e2.4 Statistical Tools for Measuring Tests Robustness.\u003c\/p\u003e \u003cp\u003e2.5 General Approaches for Robust Testing.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Linear Regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Estimating the Regression Parameters.\u003c\/p\u003e \u003cp\u003e3.3 Testing the Regression Parameters.\u003c\/p\u003e \u003cp\u003e3.4 Checking and Selecting the Model.\u003c\/p\u003e \u003cp\u003e3.5 CardiovascularRiskFactorsDataExample.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Mixed Linear Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 The MLM.\u003c\/p\u003e \u003cp\u003e4.3 Classical Estimation and Inference.\u003c\/p\u003e \u003cp\u003e4.4 Robust Estimation.\u003c\/p\u003e \u003cp\u003e4.5 Robust Inference.\u003c\/p\u003e \u003cp\u003e4.6 Checking the Model.\u003c\/p\u003e \u003cp\u003e4.7 Further Examples.\u003c\/p\u003e \u003cp\u003e4.8 Discussion and Extensions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Generalized Linear Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 The GLM.\u003c\/p\u003e \u003cp\u003e5.3 A Class of \u003ci\u003eM\u003c\/i\u003e-estimators forGLMs.\u003c\/p\u003e \u003cp\u003e5.4 Robust Inference.\u003c\/p\u003e \u003cp\u003e5.5 Breastfeeding Data Example.\u003c\/p\u003e \u003cp\u003e5.6 Doctor Visits Data Example.\u003c\/p\u003e \u003cp\u003e5.7 Discussion and Extensions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Marginal Longitudinal Data Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 The Marginal Longitudinal Data Model (MLDA) and Alternatives.\u003c\/p\u003e \u003cp\u003e6.3 A Robust GEE-type Estimator.\u003c\/p\u003e \u003cp\u003e6.4 Robust Inference.\u003c\/p\u003e \u003cp\u003e6.5 LEI Data Example.\u003c\/p\u003e \u003cp\u003e6.6 Stillbirth in Piglets Data Example.\u003c\/p\u003e \u003cp\u003e6.7 Discussion and Extensions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Survival Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 TheCox Model.\u003c\/p\u003e \u003cp\u003e7.3 Robust Estimation and Inference in the Cox Model.\u003c\/p\u003e \u003cp\u003e7.4 The Veteran’s Administration Lung Cancer Data.\u003c\/p\u003e \u003cp\u003e7.5 Structural Misspecifications.\u003c\/p\u003e \u003cp\u003e7.6 Censored Regression Quantiles.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendices.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Starting Estimators for\u003c\/b\u003e \u003cb\u003e\u003ci\u003eMM\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e-estimators of Regression Parameters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Efficiency, LRT\u003c\/b\u003e\u003cb\u003e\u003ci\u003eρ\u003c\/i\u003e\u003c\/b\u003e \u003cb\u003e, RAIC and\u003c\/b\u003e \u003cb\u003e\u003ci\u003eRC\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e\u003ci\u003ep\u003c\/i\u003e\u003c\/b\u003e \u003cb\u003ewith Biweight\u003c\/b\u003e \u003cb\u003e\u003ci\u003eρ\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e-function for the Regression Model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC An Algorithm Procedure for the Constrained\u003c\/b\u003e \u003cb\u003e\u003ci\u003eS\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e-estimator.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Some Distributions of the Exponential Family.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE Computations for the Robust GLM Estimator.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eE.1 Fisher Consistency Corrections.\u003c\/p\u003e \u003cp\u003eE.2 Asymptotic Variance.\u003c\/p\u003e \u003cp\u003eE.3 IRWLS Algorithm for Robust GLM.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eF Computations for the Robust GEE Estimator.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eF.1 IRWLS Algorithm for Robust GEE.\u003c\/p\u003e \u003cp\u003eF.2 Fisher Consistency Corrections.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eG Computation of the\u003c\/b\u003e \u003cb\u003e\u003ci\u003eCRQ.\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e \"The authors are to be congratulated for providing consulting statisticians and advanced students of statistics with an excellent guide to the rich methodology now available. Every statistician will benefit from having this book on their shelf, or, better yet, on their desk.\" (Australian \u0026amp; New Zealand Journal of Statistics, 2011)  \u003cp\u003e \"All treated methods are illustrated with several data examples. These data examples show clearly the superiority of the robust methods compared with the classical methods... However, since there exists a website with instructions for running the data examples of this book, the new robust methods can be easily applied.\" (\u003ci\u003eBiometrical Journal,\u003c\/i\u003e February 2011)\"The book by Heritier et al. is the most comprehensive and practical discussion of robust methods to date. The combination of a summary of robust methods, extensive discussion of applications, and accompanying R code give this book the potential to increase the use of robust methods in practice.\" (\u003ci\u003eJournal of Biopharmaceutical Statistics\u003c\/i\u003e, March 2010)\u003c\/p\u003e \u003cb\u003eDr Stephane Heritier\u003c\/b\u003e, NHMRC Clinical Trials Centre, University of Sydney, Australia. A senior lecturer in statistics for four years, Dr Heritier also has over a decade of research to her name, and has published numerous articles in a variety of journals. \u003cp\u003e\u003cb\u003eDr Eva Cantoni\u003c\/b\u003e, Department of Econometrics, University of Geneva, Switzerland. Also a senior lecturer in statistics, Dr Cantoni has many years teaching and research experience, and written a number journal articles.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDr Samuel Copt\u003c\/b\u003e, NHMRC Clinical Trials Centre, University of Sydney, Australia. Having completed his PhD in 2004, Dr Copt has already spent a year as a lecturer and published six journal articles. He is now a visiting scholar at the University of Sydney.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eProfessor Maria-Pia Victoria-Feser\u003c\/b\u003e, HEC Section, University of Geneva, Switzerland. Professor Victoria-Feser has over 10 years of teaching experience and has written many journal articles.\u003c\/p\u003e  Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers.  \u003cp\u003e\u003ci\u003eRobust Methods in Biostatistics\u003c\/i\u003e proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eLinear regression.\u003c\/li\u003e \u003c\/ul\u003e \u003cul\u003e \u003cli\u003eGeneralized linear models.\u003c\/li\u003e \u003c\/ul\u003e \u003cul\u003e \u003cli\u003eLinear mixed models.\u003c\/li\u003e \u003c\/ul\u003e \u003cul\u003e \u003cli\u003eMarginal longitudinal data models.\u003c\/li\u003e \u003c\/ul\u003e \u003cul\u003e \u003cli\u003eCox survival analysis model.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989970436325,"sku":"NP9780470027264","price":118.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470027264.jpg?v=1761786074","url":"https:\/\/k12savings.com\/products\/robust-methods-in-biostatistics-isbn-9780470027264","provider":"K12savings","version":"1.0","type":"link"}