Nonparametric Curve Estimation: Methods, Theory and ApplicationsSpringer Science & Business Media, 5.8.1999 - 411 sivua This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity. |
Sisältö
Preface | 1 |
Orthonormal Series and Approximation | 17 |
Density Estimation for Small Samples | 59 |
Nonparametric Regression for Small Samples | 118 |
Nonparametric Time Series Analysis for Small Samples | 181 |
Estimation of Multivariate Functions for Small Samples | 230 |
Filtering and Asymptotics | 259 |
Nonseries Methods | 323 |
Appendix A Fundamentals of Probability and Statistics | 367 |
Appendix B Software | 391 |
Author Index | 403 |
Muita painoksia - Näytä kaikki
Nonparametric Curve Estimation: Methods, Theory, and Applications Sam Efromovich Rajoitettu esikatselu - 2008 |
Nonparametric Curve Estimation: Methods, Theory, and Applications Sam Efromovich Esikatselu ei käytettävissä - 2013 |
Yleiset termit ja lausekkeet
according additive allows analysis approach approximation argument assume asymptotic basis bound calculated called changes choose coefficients consider constant controlled corner functions corresponding curve cutoff data set data-driven defined Delta density density estimation dependent diagram different discussed distribution elements equal error example Exercise experiment Explain Figure find first formula Fourier coefficients function given gives hand histogram idea implies instance integrated interval kernel linear lines look mean method minimal minimax MISE Monotone noise nonparametric normal Note observations optimal orthogonal series parameter particular performs period points possible predictors probability problem procedure random variable realizations Recall regression regression function result sample mean scale scattergram seasonal component shown shown in Figure shows signal simulations smooth space squared standard statistical Steps Strata suggest theory underlying Uniform universal estimate values variance wavelet weights write
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