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. Robust Methods in Biostatistics 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…mehr
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.
Robust Methods in Biostatistics 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:
Linear regression Generalized linear models Linear mixed models Marginal longitudinal data models Cox survival analysis model
The 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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr Stephane Heritier, 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. Dr Eva Cantoni, 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. Dr Samuel Copt, 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. Professor Maria-Pia Victoria-Feser, HEC Section, University of Geneva, Switzerland. Professor Victoria-Feser has over 10 years of teaching experience and has written many journal articles.
Inhaltsangabe
1 Introductio
1.1 What is Robust Statistics
1.2 Against What is Robust Statistics Robust
1.3 Are Diagnostic Methods an Alternative to Robust Statistics
1.4 How do Robust Statistics Compare with Other Statistical Procedures in Practice
2 Key Measures and Result
2.1 Introductio
2.2 Statistical Tools for Measuring Robustness Propertie
2.3 General Approaches for Robust Estimatio
2.4 Statistical Tools for Measuring the Robustness of Tests
2.5 General Approaches for Robust Testin
3 Linear Regressio
3.1 Introductio
3.2 Estimating the Regression Parameter
3.3 Testing the Regression Parameter
3.4 Checking and Selecting the Mode
3.5 Cardiovascular Risk Factors Data Exampl
4 Mixed Linear Model
4.1 Introductio
4.2 The MLM
4.3 Classical Estimation and Inferenc
4.4 Robust Estimatio
4.5 Robust Inferenc
4.6 Checking the Model
4.7 Further Example
4.8 Discussion and Extension
5 Generalized Linear Model
5.1 Introductio
5.2 The GL
5.3 A Class of M-estimators for GLM
5.4 Robust Inferenc
5.5 Breastfeeding Data Exampl
5.6 Doctor Visits Data Exampl
5.7 Discussion and Extension
6 Marginal Longitudinal Data Analysi
6.1 Introductio
6.2 The Marginal Longitudinal Data Model (MLDA) and Alternatives
6.3 A Robust GEE-type Estimato
6.4 Robust Inferenc
6.5 LEI Data Example
6.6 Stillbirth in Piglets Data Exampl
6.7 Discussion and Extension
7 Survival Analysi
7.1 Introductio
7.2 The Cox Mode
7.3 Robust Estimation and Inference in the Cox Mode
7.4 The Veteran's Administration Lung Cancer Dat
7.5 Structural Misspecification
7.6 Censored Regression Quantile
Appendice
A Starting Estimators for MM-estimators of Regression Parameter
B Efficiency, LRTrho , RAIC and RCp with Biweight rho-function for the Regression Mode
C An Algorithm Procedure for the Constrained S-estimato
