Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data, as well as non-Gaussian data that are transformations of latent Gaussian processes. Topics including nonparametric density estimation, nonparametric and semiparametric regression, and trend and surface estimation in…mehr
Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data, as well as non-Gaussian data that are transformations of latent Gaussian processes. Topics including nonparametric density estimation, nonparametric and semiparametric regression, and trend and surface estimation in particular for time series and spatial data are introduced. Other areas such as rapid change points and robustness are detailed alongside a study of their theoretical properties and optimality issues, such as consistency and bandwidth selection. Addressing a variety of topics, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can directly implement the formulas using any appropriate software. The overall aim of the book is to describe the methods and their theoretical backgrounds, while maintaining an analytically simple approach and including motivating examples--making it extremely useful in many sciences such as geophysics, climate research, forestry, ecology, and other natural and life sciences, as well as in finance, sociology, and engineering. * A simple and analytical description of kernel smoothing methods in various contexts * Presents the basics as well as new developments * Includes simulated and real data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics, as well as a reference book for applied statisticians and advanced researchers.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Sucharita Ghosh, PhD, is a statistician at the Swiss Federal Research Institute WSL, Switzerland. She also teaches graduate level Statistics in the Department of Mathematics, Swiss Federal Institute of Technology in Zurich. She obtained her doctorate in Statistics from the University of Toronto, Masters from the Indian Statistical Institute and B.Sc. from Presidency College, University of Calcutta, India. She was a Statistics faculty member at Cornell University and has held various short-term and long-term visiting faculty positions at universities such as the University of North Carolina at Chapel Hill and University of York, UK. She has also taught Statistics to undergraduate and graduate students at a number of universities, namely in Canada (Toronto), USA (Cornell, UNC Chapel Hill), UK (York), Germany (Konstanz) and Switzerland (ETH Zurich). Her research interests include smoothing, integral transforms, time series and spatial data analysis, having applications in a number of areas including the natural sciences, finance and medicine among others.
Inhaltsangabe
Preface ix Density Estimation 1 1.1 Introduction 1 1.1.1 Orthogonal polynomials 2 1.2 Histograms 8 1.2.1 Properties of the histogram 9 1.2.2 Frequency polygons 14 1.2.3 Histogram bin widths 15 1.2.4 Average shifted histogram 19 1.3 Kernel density estimation 19 1.3.1 Naive density estimator 21 1.3.2 Parzen-Rosenblatt kernel density estimator 25 1.3.3 Bandwidth selection 43 1.4 Multivariate density estimation 53 Nonparametric Regression 59 2.1 Introduction 59 2.1.1 Method of least squares 60 2.1.2 Influential observations 70 2.1.3 Nonparametric regression estimators 71 2.2 Priestley-Chao regression estimator 73 2.2.1 Weak consistency 77 2.3 Local polynomials 80 2.3.1 Equivalent kernels 84 2.4 Nadaraya-Watson regression estimator 87 2.5 Bandwidth selection 93 2.6 Further remarks 99 2.6.1 Gasser-M¿uller estimator 99 2.6.2 Smoothing splines 100 2.6.3 Kernel efficiency 103 Trend Estimation 105 3.1 Time series replicates 105 3.1.1 Model 111 3.1.2 Estimation of common trend function 114 3.1.3 Asymptotic properties 114 3.2 Irregularly spaced observations 120 3.2.1 Model 122 3.2.2 Derivatives, distribution function, and quantiles 125 3.2.3 Asymptotic properties 129 3.2.4 Bandwidth selection 137 3.3 Rapid change points 141 3.3.1 Model and definition of rapid change 144 3.3.2 Estimation and asymptotics 145 3.4 Nonparametric M-estimation of a trend function 149 3.4.1 Kernel-based M-estimation 149 3.4.2 Local polynomial M-estimation 154 Semiparametric Regression 157 4.1 Partial linear models with constant slope 157 4.2 Partial linear models with time-varying slope 160 4.2.1 Estimation 165 4.2.2 Assumptions 166 4.2.3 Asymptotics 171 Surface Estimation 181 5.1 Introduction 181 5.2 Gaussian subordination 193 5.3 Spatial correlations 195 5.4 Estimation of the mean and consistency 197 5.4.1 Asymptotics 197 5.5 Variance estimation 203 5.6 Distribution function and spatial Gini index 206 5.6.1 Asymptotics 213 References 217 Author Index 243 Subject Index 251
