Quantile Regression in Clinical Research : Complete analysis for data at a loss of homogeneity /

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 spreadi...

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Bibliographic Details
Main Authors:	Cleophas, Ton J. (Author), Zwinderman, Aeilko H. (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:	1st ed. 2021.
Subjects:	Clinical medicine-Research. Statistics . Data mining. Big data. Clinical Research. Applied Statistics. Data Mining and Knowledge Discovery. Big Data. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.