Quantile regression is an approach to data at a loss of homogeneity, for example (1) data with outliers, (2) skewed data like corona - deaths data, (3) data with inconstant variability, (4) big data. In clinical research many examples can be given like circadian phenomena, and diseases where spreading may be dependent on subsets with frailty, low weight, low hygiene, and many forms of lack of healthiness. Stratified analyses is the laborious and rather explorative way of analysis, but quantile analysis is a more fruitful, faster and completer alternative for the purpose. Considering all of this, we are on the verge of a revolution in data analysis. The current edition is the first textbook and tutorial of quantile regressions for medical and healthcare students as well as recollection/update bench, and help desk for professionals. Each chapter can be studied as a standalone and covers one of the many fields in the fast growing world of quantile regressions. Step by step analyses of over 20 data files stored at extras.springer.com are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology(2000-2002). From their expertise they should be able to make adequate selections of modern quantile regression methods for the benefit of physicians, students, and investigators.
Edition
Language
Place of publication
Publishing group
Springer International Publishing
Illustrations
1 s/w Abbildung
XII, 290 p. 1 illus. With online files/update.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 21 mm
Weight
ISBN-13
978-3-030-82839-4 (9783030828394)
DOI
10.1007/978-3-030-82840-0
Schweitzer Classification
Ton J Cleophas is internist-clinical pharmacologist at the Department of Medicine Albert Schweitzer Hospital Dordrecht the Netherlands. He is also professor of Statistics and member of the Scientific Committee of the European College of Pharmaceutical Medicine Lyon France. He is particularly interested in machine learning methodologies and published many complete-overview-textbooks of the subject.
Aeilko H Zwinderman is professor of Statistics and Chair of the Department of Biostatistics and Epidemiology at the University of Amsterdam the Netherlands. His current work focuses on development and validation of multivariable models, particularly in genetic research, and he is a major developer of penalized canonical analysis.
Chapter 1. General Introduction.- Chapter 2. Mathematical Models for Separating Quantiles from One Another.- Part I: Simple Univariate Regressions versus Quantile.- Chapter 3. Traditional and Robust Regressions versus Quantile.- Chapter 4. Autoregressions versus quantile.- Chapter 5. Discrete Trend Analysis versus Quantile.- Chapter 6. Continuous Trend Analysis versus Quantile.- Binary Poisson / Negative Binomial Regression versus Quantile.- Chapter 8. Robust Standard Errors Regressions versus Quantile.- Chapter 9. Optimal Scaling versus Quantile Regression.- Chapter 10. Intercept only Poisson Regression versus Quantile.- Part II: Multiple Variables Regressions versus Quantile.- Chapter 11. Four Predictors Regressions versus Quantile.- Chapter 12. Gene Expressions Regressions, Traditional versus Quantile.- Chapter 13. Koenker's Multiple Variables Regression with Quantile.- Chapter 14. Interaction Adjusted Regression versus Quantile.- Chapter 15. Quantile Regression to Study Corona Deaths.- Chapter 16. Laboratory Values Predict Survival Sepsis, Traditional Regression versus Quantile.- Chapter 17. Multinomial Poisson Regression versus Quantile.- Chapter 18. Regressions with Inconstant Variability versus Quantile.- Chapter 19. Restructuring Categories into Multiple Dummy Variables versus Quantile.- Chapter 20. Poisson Events per Person per Period of Time versus Quantile.- Part III: Special Regressions versus Quantile.- Chapter 21. Two Stage Least Squares Regressions versus Quantile.- Chapter 22. Partial Correlations versus Quantile Regressions.- Chapter 23. Random Intercept Regression versus Quantile.- Chapter 24. Regression Trees versus Quantile.- Chapter 25. Kernel Regression versus Quantile.- Chapter 26. Quasi-likelihood Regression versus Quantile.- Chapter 27. Summaries.
