Applied Nonparametric Regression
Cambridge University Press, 1990 - 333 Seiten
Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. Härdle argues that all smoothing methods are based on a local averaging mechanism and can be seen as essentially equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this exposition, various other smoothing methods (among them splines and orthogonal polynomials) are presented and their merits discussed. All the methods presented can be understood on an intuitive level; however, exercises and supplemental materials are provided for those readers desiring a deeper understanding of the techniques. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics as well as from other disciplines including medicine and engineering.
Was andere dazu sagen - Rezension schreiben
Es wurden keine Rezensionen gefunden.
Basic idea of smoothing
How close is the smooth to the true curve?
Choosing the smoothing parameter
Data sets with outliers
Nonparametric regression techniques
Andere Ausgaben - Alle anzeigen
algorithm approximation asymptotically optimal average bandwidth h bias bootstrap boundary canonical kernels computed confidence bands confidence intervals consider constant cross-validation dA(h dashed line defined denotes density estimation derivative distribution Engel curve Epanechnikov example Family Expenditure Survey Figure Gasser Härdle Hölder continuous k-NN smoother kernel estimator kernel function kernel K(u kernel smoother kernel weights label M-smoother ma(r ma(z marginal density Marron mean squared error minimizes Nadaraya-Watson neighborhood nonparametric regression nonparametric smoothing normal optimal bandwidth optimal rate outliers parametric model points pointwise polynomial potato versus predictor variable problem procedure quartic kernel random rate of convergence regression curve regression function regressogram residuals response variables sample shows simulated data set smoothing parameter smoothing techniques solid line spline spline smoothing Statistical stochastic Table tends to zero Theorem transformations values variance vector versus net income weight function weight sequence Whi(z wild bootstrap workunit X-variables XploRe
Alle Ergebnisse von Google Books »