Functional estimation for density, regression models and processes /:
This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators fo...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore :
World Scientific,
©2011.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators. It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators. |
| Beschreibung: | 1 online resource (ix, 199 pages) |
| Bibliographie: | Includes bibliographical references (pages 191-196) and index. |
| ISBN: | 9789814343749 9814343749 1283235048 9781283235044 9786613235046 6613235040 |
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| 504 | |a Includes bibliographical references (pages 191-196) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 1. Introduction. 1.1. Estimation of a density. 1.2. Estimation of a regression curve. 1.3. Estimation of functionals of processes. 1.4. Content of the book -- 2. Kernel estimator of a density. 2.1. Introduction. 2.2. Risks and optimal bandwidths for the kernel estimator. 2.3. Weak convergence. 2.4. Minimax and histogram estimators. 2.5. Estimation of functionals of a density. 2.6. Density of absolutely continuous distributions. 2.7. Hellinger distance between a density and its estimator. 2.8. Estimation of the density under right-censoring. 2.9. Estimation of the density of left-censored variables. 2.10. Kernel estimator for the density of a process. 2.11. Exercises -- 3. Kernel estimator of a regression function. 3.1. Introduction and notation. 3.2. Risks and convergence rates for the estimator. 3.3. Optimal bandwidths. 3.4. Weak convergence of the estimator. 3.5. Estimation of a regression curve by local polynomials. 3.6. Estimation in regression models with functional variance. 3.7. Estimation of the mode of a regression function. 3.8. Estimation of a regression function under censoring. 3.9. Proportional odds model. 3.10. Estimation for the regression function of processes. 3.11. Exercises -- 4. Limits for the varying bandwidths estimators. 4.1. Introduction. 4.2. Estimation of densities. 4.3. Estimation of regression functions. 4.4. Estimation for processes. 4.5. Exercises -- 5. Nonparametric estimation of quantiles. 5.1. Introduction. 5.2. Asymptotics for the quantile processes. 5.3. Bandwidth selection. 5.4. Estimation of the conditional density of Y given X. 5.5. Estimation of conditional quantiles for processes. 5.6. Inverse of a regression function. 5.7. Quantile function of right-censored variables. 5.8. Conditional quantiles with variable bandwidth. 5.9. Exercises -- 6. Nonparametric estimation of intensities for stochastic processes. 6.2. Introduction. 6.2. Risks and convergences for estimators of the intensity. 6.3. Risks and convergences for multiplicative intensities. 6.4. Histograms for intensity and regression functions. 6.5. Estimation of the density of duration excess. 6.6. Estimators for processes on increasing intervals. 6.7. Models with varying intensity or regression coefficients. 6.8. Progressive censoring of a random time sequence. 6.9. Exercises -- 7. Estimation in semi-parametric regression models. 7.1. Introduction. 7.2. Convergence of the estimators. 7.3. Nonparametric regression with a change of variables. 7.4. Exercises -- 8. Diffusion processes. 8.1. Introduction. 8.2. Estimation for continuous diffusions by discretization. 8.3. Estimation for continuous diffusion processes. 8.4. Estimation of discretely observed diffusions with jumps. 8.5. Continuous estimation for diffusions with jumps. 8.6. Transformations of a non-stationary Gaussian process. 8.7. Exercises -- 9. Applications to time series. 9.1. Nonparametric estimation of the mean. 9.2. Periodic models for time series. 9.3. Nonparametric estimation of the covariance function. 9.4. Nonparametric transformations for stationarity. 9.5. Change-points in time series. 9.6. Exercises. | |
| 520 | |a This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators. It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Estimation theory. |0 http://id.loc.gov/authorities/subjects/sh85044957 | |
| 650 | 0 | |a Nonparametric statistics. |0 http://id.loc.gov/authorities/subjects/sh85092349 | |
| 650 | 2 | |a Statistics, Nonparametric |0 https://id.nlm.nih.gov/mesh/D018709 | |
| 650 | 6 | |a Théorie de l'estimation. | |
| 650 | 6 | |a Statistique non paramétrique. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Estimation theory |2 fast | |
| 650 | 7 | |a Nonparametric statistics |2 fast | |
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| contents | 1. Introduction. 1.1. Estimation of a density. 1.2. Estimation of a regression curve. 1.3. Estimation of functionals of processes. 1.4. Content of the book -- 2. Kernel estimator of a density. 2.1. Introduction. 2.2. Risks and optimal bandwidths for the kernel estimator. 2.3. Weak convergence. 2.4. Minimax and histogram estimators. 2.5. Estimation of functionals of a density. 2.6. Density of absolutely continuous distributions. 2.7. Hellinger distance between a density and its estimator. 2.8. Estimation of the density under right-censoring. 2.9. Estimation of the density of left-censored variables. 2.10. Kernel estimator for the density of a process. 2.11. Exercises -- 3. Kernel estimator of a regression function. 3.1. Introduction and notation. 3.2. Risks and convergence rates for the estimator. 3.3. Optimal bandwidths. 3.4. Weak convergence of the estimator. 3.5. Estimation of a regression curve by local polynomials. 3.6. Estimation in regression models with functional variance. 3.7. Estimation of the mode of a regression function. 3.8. Estimation of a regression function under censoring. 3.9. Proportional odds model. 3.10. Estimation for the regression function of processes. 3.11. Exercises -- 4. Limits for the varying bandwidths estimators. 4.1. Introduction. 4.2. Estimation of densities. 4.3. Estimation of regression functions. 4.4. Estimation for processes. 4.5. Exercises -- 5. Nonparametric estimation of quantiles. 5.1. Introduction. 5.2. Asymptotics for the quantile processes. 5.3. Bandwidth selection. 5.4. Estimation of the conditional density of Y given X. 5.5. Estimation of conditional quantiles for processes. 5.6. Inverse of a regression function. 5.7. Quantile function of right-censored variables. 5.8. Conditional quantiles with variable bandwidth. 5.9. Exercises -- 6. Nonparametric estimation of intensities for stochastic processes. 6.2. Introduction. 6.2. Risks and convergences for estimators of the intensity. 6.3. Risks and convergences for multiplicative intensities. 6.4. Histograms for intensity and regression functions. 6.5. Estimation of the density of duration excess. 6.6. Estimators for processes on increasing intervals. 6.7. Models with varying intensity or regression coefficients. 6.8. Progressive censoring of a random time sequence. 6.9. Exercises -- 7. Estimation in semi-parametric regression models. 7.1. Introduction. 7.2. Convergence of the estimators. 7.3. Nonparametric regression with a change of variables. 7.4. Exercises -- 8. Diffusion processes. 8.1. Introduction. 8.2. Estimation for continuous diffusions by discretization. 8.3. Estimation for continuous diffusion processes. 8.4. Estimation of discretely observed diffusions with jumps. 8.5. Continuous estimation for diffusions with jumps. 8.6. Transformations of a non-stationary Gaussian process. 8.7. Exercises -- 9. Applications to time series. 9.1. Nonparametric estimation of the mean. 9.2. Periodic models for time series. 9.3. Nonparametric estimation of the covariance function. 9.4. Nonparametric transformations for stationarity. 9.5. Change-points in time series. 9.6. Exercises. |
| ctrlnum | (OCoLC)754793520 |
| dewey-full | 519.544 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.544 |
| dewey-search | 519.544 |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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Introduction. 1.1. Estimation of a density. 1.2. Estimation of a regression curve. 1.3. Estimation of functionals of processes. 1.4. Content of the book -- 2. Kernel estimator of a density. 2.1. Introduction. 2.2. Risks and optimal bandwidths for the kernel estimator. 2.3. Weak convergence. 2.4. Minimax and histogram estimators. 2.5. Estimation of functionals of a density. 2.6. Density of absolutely continuous distributions. 2.7. Hellinger distance between a density and its estimator. 2.8. Estimation of the density under right-censoring. 2.9. Estimation of the density of left-censored variables. 2.10. Kernel estimator for the density of a process. 2.11. Exercises -- 3. Kernel estimator of a regression function. 3.1. Introduction and notation. 3.2. Risks and convergence rates for the estimator. 3.3. Optimal bandwidths. 3.4. Weak convergence of the estimator. 3.5. Estimation of a regression curve by local polynomials. 3.6. Estimation in regression models with functional variance. 3.7. 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| id | ZDB-4-EBA-ocn754793520 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:18:01Z |
| institution | BVB |
| isbn | 9789814343749 9814343749 1283235048 9781283235044 9786613235046 6613235040 |
| language | English |
| oclc_num | 754793520 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (ix, 199 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | World Scientific, |
| record_format | marc |
| spelling | Pons, Odile. Functional estimation for density, regression models and processes / Odile Pons. Singapore : World Scientific, ©2011. 1 online resource (ix, 199 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Includes bibliographical references (pages 191-196) and index. Print version record. 1. Introduction. 1.1. Estimation of a density. 1.2. Estimation of a regression curve. 1.3. Estimation of functionals of processes. 1.4. Content of the book -- 2. Kernel estimator of a density. 2.1. Introduction. 2.2. Risks and optimal bandwidths for the kernel estimator. 2.3. Weak convergence. 2.4. Minimax and histogram estimators. 2.5. Estimation of functionals of a density. 2.6. Density of absolutely continuous distributions. 2.7. Hellinger distance between a density and its estimator. 2.8. Estimation of the density under right-censoring. 2.9. Estimation of the density of left-censored variables. 2.10. Kernel estimator for the density of a process. 2.11. Exercises -- 3. Kernel estimator of a regression function. 3.1. Introduction and notation. 3.2. Risks and convergence rates for the estimator. 3.3. Optimal bandwidths. 3.4. Weak convergence of the estimator. 3.5. Estimation of a regression curve by local polynomials. 3.6. Estimation in regression models with functional variance. 3.7. Estimation of the mode of a regression function. 3.8. Estimation of a regression function under censoring. 3.9. Proportional odds model. 3.10. Estimation for the regression function of processes. 3.11. Exercises -- 4. Limits for the varying bandwidths estimators. 4.1. Introduction. 4.2. Estimation of densities. 4.3. Estimation of regression functions. 4.4. Estimation for processes. 4.5. Exercises -- 5. Nonparametric estimation of quantiles. 5.1. Introduction. 5.2. Asymptotics for the quantile processes. 5.3. Bandwidth selection. 5.4. Estimation of the conditional density of Y given X. 5.5. Estimation of conditional quantiles for processes. 5.6. Inverse of a regression function. 5.7. Quantile function of right-censored variables. 5.8. Conditional quantiles with variable bandwidth. 5.9. Exercises -- 6. Nonparametric estimation of intensities for stochastic processes. 6.2. Introduction. 6.2. Risks and convergences for estimators of the intensity. 6.3. Risks and convergences for multiplicative intensities. 6.4. Histograms for intensity and regression functions. 6.5. Estimation of the density of duration excess. 6.6. Estimators for processes on increasing intervals. 6.7. Models with varying intensity or regression coefficients. 6.8. Progressive censoring of a random time sequence. 6.9. Exercises -- 7. Estimation in semi-parametric regression models. 7.1. Introduction. 7.2. Convergence of the estimators. 7.3. Nonparametric regression with a change of variables. 7.4. Exercises -- 8. Diffusion processes. 8.1. Introduction. 8.2. Estimation for continuous diffusions by discretization. 8.3. Estimation for continuous diffusion processes. 8.4. Estimation of discretely observed diffusions with jumps. 8.5. Continuous estimation for diffusions with jumps. 8.6. Transformations of a non-stationary Gaussian process. 8.7. Exercises -- 9. Applications to time series. 9.1. Nonparametric estimation of the mean. 9.2. Periodic models for time series. 9.3. Nonparametric estimation of the covariance function. 9.4. Nonparametric transformations for stationarity. 9.5. Change-points in time series. 9.6. Exercises. This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators. It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators. English. Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Nonparametric statistics. http://id.loc.gov/authorities/subjects/sh85092349 Statistics, Nonparametric https://id.nlm.nih.gov/mesh/D018709 Théorie de l'estimation. Statistique non paramétrique. MATHEMATICS Probability & Statistics General. bisacsh Estimation theory fast Nonparametric statistics fast has work: Functional estimation for density, regression models and processes (Text) https://id.oclc.org/worldcat/entity/E39PCH3pb3b3t4rhKJgKCQ4tqP https://id.oclc.org/worldcat/ontology/hasWork Print version: Pons, Odile. Functional estimation for density, regression models and processes. Singapore : World Scientific, ©2011 9789814343732 (OCoLC)694395311 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=389633 Volltext |
| spellingShingle | Pons, Odile Functional estimation for density, regression models and processes / 1. Introduction. 1.1. Estimation of a density. 1.2. Estimation of a regression curve. 1.3. Estimation of functionals of processes. 1.4. Content of the book -- 2. Kernel estimator of a density. 2.1. Introduction. 2.2. Risks and optimal bandwidths for the kernel estimator. 2.3. Weak convergence. 2.4. Minimax and histogram estimators. 2.5. Estimation of functionals of a density. 2.6. Density of absolutely continuous distributions. 2.7. Hellinger distance between a density and its estimator. 2.8. Estimation of the density under right-censoring. 2.9. Estimation of the density of left-censored variables. 2.10. Kernel estimator for the density of a process. 2.11. Exercises -- 3. Kernel estimator of a regression function. 3.1. Introduction and notation. 3.2. Risks and convergence rates for the estimator. 3.3. Optimal bandwidths. 3.4. Weak convergence of the estimator. 3.5. Estimation of a regression curve by local polynomials. 3.6. Estimation in regression models with functional variance. 3.7. Estimation of the mode of a regression function. 3.8. Estimation of a regression function under censoring. 3.9. Proportional odds model. 3.10. Estimation for the regression function of processes. 3.11. Exercises -- 4. Limits for the varying bandwidths estimators. 4.1. Introduction. 4.2. Estimation of densities. 4.3. Estimation of regression functions. 4.4. Estimation for processes. 4.5. Exercises -- 5. Nonparametric estimation of quantiles. 5.1. Introduction. 5.2. Asymptotics for the quantile processes. 5.3. Bandwidth selection. 5.4. Estimation of the conditional density of Y given X. 5.5. Estimation of conditional quantiles for processes. 5.6. Inverse of a regression function. 5.7. Quantile function of right-censored variables. 5.8. Conditional quantiles with variable bandwidth. 5.9. Exercises -- 6. Nonparametric estimation of intensities for stochastic processes. 6.2. Introduction. 6.2. Risks and convergences for estimators of the intensity. 6.3. Risks and convergences for multiplicative intensities. 6.4. Histograms for intensity and regression functions. 6.5. Estimation of the density of duration excess. 6.6. Estimators for processes on increasing intervals. 6.7. Models with varying intensity or regression coefficients. 6.8. Progressive censoring of a random time sequence. 6.9. Exercises -- 7. Estimation in semi-parametric regression models. 7.1. Introduction. 7.2. Convergence of the estimators. 7.3. Nonparametric regression with a change of variables. 7.4. Exercises -- 8. Diffusion processes. 8.1. Introduction. 8.2. Estimation for continuous diffusions by discretization. 8.3. Estimation for continuous diffusion processes. 8.4. Estimation of discretely observed diffusions with jumps. 8.5. Continuous estimation for diffusions with jumps. 8.6. Transformations of a non-stationary Gaussian process. 8.7. Exercises -- 9. Applications to time series. 9.1. Nonparametric estimation of the mean. 9.2. Periodic models for time series. 9.3. Nonparametric estimation of the covariance function. 9.4. Nonparametric transformations for stationarity. 9.5. Change-points in time series. 9.6. Exercises. Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Nonparametric statistics. http://id.loc.gov/authorities/subjects/sh85092349 Statistics, Nonparametric https://id.nlm.nih.gov/mesh/D018709 Théorie de l'estimation. Statistique non paramétrique. MATHEMATICS Probability & Statistics General. bisacsh Estimation theory fast Nonparametric statistics fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85044957 http://id.loc.gov/authorities/subjects/sh85092349 https://id.nlm.nih.gov/mesh/D018709 |
| title | Functional estimation for density, regression models and processes / |
| title_auth | Functional estimation for density, regression models and processes / |
| title_exact_search | Functional estimation for density, regression models and processes / |
| title_full | Functional estimation for density, regression models and processes / Odile Pons. |
| title_fullStr | Functional estimation for density, regression models and processes / Odile Pons. |
| title_full_unstemmed | Functional estimation for density, regression models and processes / Odile Pons. |
| title_short | Functional estimation for density, regression models and processes / |
| title_sort | functional estimation for density regression models and processes |
| topic | Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Nonparametric statistics. http://id.loc.gov/authorities/subjects/sh85092349 Statistics, Nonparametric https://id.nlm.nih.gov/mesh/D018709 Théorie de l'estimation. Statistique non paramétrique. MATHEMATICS Probability & Statistics General. bisacsh Estimation theory fast Nonparametric statistics fast |
| topic_facet | Estimation theory. Nonparametric statistics. Statistics, Nonparametric Théorie de l'estimation. Statistique non paramétrique. MATHEMATICS Probability & Statistics General. Estimation theory Nonparametric statistics |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=389633 |
| work_keys_str_mv | AT ponsodile functionalestimationfordensityregressionmodelsandprocesses |