Quantile Regression in Clinical Research

Complete analysis for data at a loss of homogeneity

AvTon J. Cleophas,Aeilko H. Zwinderman

Inbunden, Engelska, 2022

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Häftad

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Beskrivning

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. Step by step analyses of over 20 data files stored at extras.springer.com are included for self-assessment.

Produktinformation

Utgivningsdatum:2022-01-18
Mått:155 x 235 x undefined mm
Format:Inbunden
Språk:Engelska
Antal sidor:290
Förlag:Springer Nature Switzerland AG
ISBN:9783030828394

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Matematisk statistik inom Naturvetenskap och teknik
Databaser inom Data och IT

Mer om författaren

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.

Innehållsförteckning

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.