Front Cover; Contents; Preface; Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis; Chapter 1: Introduction; Chapter 2: A Concept of Approximation; Chapter 3: Discrete Models; Chapter 4: Outliers; Chapter 5: The Location-scale Problem; Chapter 6: The Analysis of Variance; Chapter 7: Non-parametric Regression: Location; Chapter 8: Non-parametric Regression: Scale; Chapter 9: Image Analysis; Chapter 10: Non-parametric Densities; Chapter 11: A Critique of Statistics; Bibliography.;"This book presents a philosophical study of statistics via the concept of data approximation. Developed by the well-regarded author, this approach discusses how analysis must take into account that models are, at best, an approximation of real data. It is, therefore, closely related to robust statistics and nonparametric statistics and can be used to study nearly any statistical technique. The book also includes an interesting discussion of the frequentist versus Bayesian debate in statistics."--