Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus IllustrationsOUP Oxford, 14 aug 1997 - 204 pagina's The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus. |
Inhoudsopgave
1 Density estimation for exploring data | 1 |
2 Density estimation for inference | 25 |
3 Nonparametric regression for exploring data | 48 |
4 Inference with nonparametric regression | 69 |
5 Checking parametric regression models | 86 |
6 Comparing curves and surfaces | 107 |
7 Time series data | 129 |
Veelvoorkomende woorden en zinsdelen
aircraft span data Amer analysis approach approximate assess associated asymptotic autocorrelation Azzalini bandwidth Barrier Reef bias bootstrap Bowman catch score Chapter component computed constructed contours correlation covariate cross-validation defined denotes density estimate density function depth design points Diggle discussed effect F statistic Figure fitted following S-Plus code graphical Härdle Hastie and Tibshirani kernel function laryngeal cancer latitude left panel linear model linear regression local linear Log span logistic regression logit longitude Marron matrix method multivariate nearest neighbour nonparametric estimate nonparametric regression nonparametric regression curve normal distribution observed optimal smoothing parameter p-value panel of Fig parameter h parametric model plot quadratic radiocarbon reconstruct Fig reference band regression function regression model residual sum right panel S-Plus Illustration sample scatterplot simple simulated sm.density sm.regression smoothing parameter spline techniques tephra tephra data test statistic two-dimensional value of h variability bands variable bandwidth vector weights
Verwijzingen naar dit boek
Nonparametric Simple Regression: Smoothing Scatterplots, Nummer 130 John Fox Gedeeltelijke weergave - 2000 |