Smoothing Methods in StatisticsSpringer Science & Business Media, 6 dic 2012 - 340 páginas The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue. |
Índice
1 | |
Simple Univariate Density Estimation | 13 |
3 | 21 |
Smoother Univariate Density Estimation | 40 |
5 | 67 |
Multivariate Density Estimation | 121 |
Nonparametric Regression | 134 |
Smoothing Ordered Categorical Data | 215 |
Further Applications of Smoothing | 252 |
Appendices | 275 |
290 | |
96 | 312 |
321 | |
329 | |
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additive model American Statistical Association AMISE Annals of Statistics asymptotic asymptotically optimal autocorrelation bandwidth bandwidth selection Bayesian bin-width bins bivariate boundary bias boundary kernel CD rate cell Computational Construct contingency table corresponding cross-validation curse of dimensionality data set Data source Description of data directory of statlib discriminant analysis distribution esti estimate based Figure File name frequency estimates frequency polygon Gasser Gaussian kernel Härdle Hellinger distance histogram Jones kernel density estimation kernel estimator kernel function least squares likelihood estimation linear estimate locally varying loess loess estimate Marron matrix minimizer MISE Monte Carlo multivariate nearest neighbor nonparametric regression observations optimal outliers penalized likelihood polynomial estimators predictors probability probability vector projection pursuit properties proposed regression curve regression estimate roughness penalty S-PLUS sample Scatter plot selector showed Simonoff smoother smoothing parameter sparse spline estimate superimposed tests timate undersmoothing univariate values Variables in file variance Wahba Wand width zero