1 Introduction
2 Basic definitions
3 Features of a distribution and statistical modelling
4 Sample quantiles
5 Computation of quantiles
6 Topics in inference
7 Standard errors, confidence intervals, and tests of hypotheses
8 Linear quantile regression
9 Nonlinear quantile regression
10 Quantile regression for discrete responses
11 Quantile-based distributional comparisons
12 Regression models for distribution functions
13 Quantile regression with correlated observations
Biography
Marco Geraci is Professor of Statistics and Medical Statistics at Sapienza University of Rome and an internationally recognised expert in quantile-based statistical methods. His research focuses on the development of flexible modelling approaches for complex data, with applications in the health and social sciences. He has held academic positions at University College London, the University of Manchester, and the University of South Carolina. His work has been widely cited, with several publications recognised as highly cited contributions in statistics. Professor Geraci was Statistical Editor of the Journal of Child Health Care and has served on the editorial boards of leading statistical journals, including the Journal of the Royal Statistical Society Series A. He is a Fellow of the Royal Statistical Society and a member of the editorial board of Significance. He is also the author of widely used R packages for quantile modelling, which have contributed to the uptake of modern statistical methods in applied research.
