Overview

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.  

Full Product Details

Author:   Ton J. Cleophas ,  Aeilko H. Zwinderman
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Weight:   0.555kg
ISBN:  

9783030828424

ISBN 10:   3030828425
Pages:   290
Publication Date:   19 January 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

3. Separating quantiles, traditional and quantile-wise 4. Special case Chapter 2 Mathematical models for separating quantiles from one another             1.  Introduction             2. Maximizing linear functions with the help of support vectors             3. Maximizing linear function with the help of a quadratic Lagrangian multiplier method             4. Maximizing linear function wit the help of simplex algorithms             5. The intuition of quantile regression   Part I Univariate quantile regression Chapter 3 Quantile regressions for data at a loss of homogeneity             1. Introduction             2. Traditional linear and robust linear regression analysis             3. Quantile linear regression             4. Conclusion   Chapter 4 Quantile regressions for bimodal outcome data             1. Introduction              2. ARIMA (autoregressive integrating moving average) autoregression methodology             3. Quantile regressions for autoregressive data             4. Conclusion Chapter 5 Chi-square test for trends versus quantile regression (Chap.15)             1. Introduction             2. Chi-square testing for trend analysis             3. Quantile regressions for trend analysis             4. Conclusion Chapter 6 One way anova for trends versus quantile regression             1. Introduction             2. One way anova for testing event rates             3. Quantile regression for testing event rates             4. Conclusion Chapter 7 Poisson regressions for event rates versus quantile regressions             1. Introduction             2. Poisson regression for testing event rates             3. Quantile regressions for testing event rates             4. Conclusion Chapter 8 Poisson regressions for event outcomes per population versus quantile regression             1. Introduction             2. Poisson regression for event outcomes per population             3. Quantile regression for event outcomes per population             4. Conclusion   Chapter 9 Quasi-likelihood regressions versus quantile regressions             1. Introduction             2. Quasi-likelihood regressions             3. Quantile regressions             4. Conclusion Chapter 10 Binary Poisson regression and Negative binomial regression versus quantile regression             1. Introduction             2. Binary Poisson regression and negative binomial regression             3. Quantile regression             4. Conclusion Chapter 11 Paired McNemar versus quantile regression             1. Introduction             2. Mc Nemar's tests for analysis of paired binary data             3. Quantile regression for analysis of paired binary data             4. Conclusion   Part II Multiple variables quantile regression Chapter 12 Multiple ordinary least squares (OLS) versus quantile regressions             1. Introduction             2. Gene expression levels predict drug efficacy scores             3. Ordinary least squares regression versus quantile regression for the purpose             4. Conclusion Chapter 13 Partial correlations versus quantile regressions             1. Introduction             2. Exercise and calorieintake and their interaction predict weightloss             3. Partial correlations and qunatile regressions for analysis             4. Conclusion Chapter 14 Quantile regression to study Corona-mortality             1. Introduction             2. Obesity, age, urbanization, capita income predict corona deaths             3. Ordinary least squares as compared to quantile regressions for analysis             4. Conclusion Chapter 15 Graphical approach to quantile regressions and continuous outcomes             1. Introduction             2. Traditional multiple variables linear regression for analysis             3. Quantile regression for analysis             4. Conclusion Chapter 16 Graphical approach to quantile regressions and binary outcomes             1. Introduction             2. Laboratory values predict survival from sepsis             3. Logistic regression versus quantile regression for analysis             4. Conclusion Chapter 17 Loglinear models for incident risks versus quantile regressions             1. Introduction             2. Loglinear models for incident risks             3. Quantile regression for the same             4. Conclusion Chapter 18 Adjusted Poisson regressions for event rates versus quantile regressions             1. Introduction             2. Adjusted Poisson regression for event rates             3. Quantile regressions for event rates             4. Conclusion Chapter 19 Poisson event outcomes per person per period of time versus quantile regression             1. Introduction             2. Poisson event outcomes per person per period of time             3. Quantile regression event outcomes per person per period of time             4. Conclusion Chapter 20 Restructuring categories into multiple binary variables versus quantile regression             1.Introduction             2. Restructuring categories into multiple binary variables             3. Quantile regressions             4. Conclusions Chapter 21 Variance components analysis versus quantile regressions             1. Introduction             2. Variance components analysis             3. Quantile regressions             4. Conclusion Chapter 22 Contrast coefficients analysis versus quantile regressions             1. Introduction             2. Contrast coefficients             3. Quantile regressions             4. Conclusion Chapter 23 Dichotomous multiple regression versus quantile regression             1. Introduction             2. Dichotomous multiple regression             3. Quantile regression             4. Conclusion Chapter 24 Probit regression versus quantile regressions             1. Introduction             2. Probit regression             3. Quantile regression             4. Conclusion Chapter 25 Summaries and abstracts Index (200-250 pages)

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Author Information

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. 

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