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From nonparametric regression to statistical inference for non-ergodic diffusion processes / Nicolas Marie
| From nonparametric regression to statistical inference for non-ergodic diffusion processes / Nicolas Marie |
| Autore | Marie, Nicolas |
| Pubbl/distr/stampa | Cham, : Springer, 2025 |
| Descrizione fisica | 1 testo elettronico (xii, 184 p. : ill.) |
| Soggetto topico |
60H10 - Stochastic ordinary differential equations [MSC 2020]
60J60 - Diffusion processes [MSC 2020] 62G05 - Nonparametric estimation [MSC 2020] 62M05 - Markov processes: estimation; hidden Markov models [MSC 2020] 62M09 - Non-Markovian processes: estimation [MSC 2020] |
| Soggetto non controllato |
Fractional Brownian Motion
Lévy processes Model selection Nonparametric Estimation PCO method Stochastic Differential Equations |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00311226 |
Marie, Nicolas
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| Cham, : Springer, 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Non-Gaussian Selfsimilar Stochastic Processes / Ciprian Tudor
| Non-Gaussian Selfsimilar Stochastic Processes / Ciprian Tudor |
| Autore | Tudor, Ciprian |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | xii, 101 p. : ill. ; 24 cm |
| Soggetto topico |
60-XX - Probability theory and stochastic processes [MSC 2020]
60G18 - Self-similar stochastic processes [MSC 2020] 60G22 - Fractional processes, including fractional Brownian motion [MSC 2020] 60H05 - Stochastic integrals [MSC 2020] 60H15 - Stochastic partial differential equations (aspects of stochastic analysis) [MSC 2020] 62M09 - Non-Markovian processes: estimation [MSC 2020] |
| Soggetto non controllato |
Hermite Processes
Hermite noise Mulitple Stochastic Integrals Non-Gaussian Stochastic Processes Parameter Estimation Selfsimilar Stochastic Processes Stochastic Differential Equations Stochastic Integration of Hermite Processes Wiener chaos |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00279547 |
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Tudor, Ciprian
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| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Nonparametric Statistics for Stochastic Processes : Estimation and Prediction / D. Bosq
| Nonparametric Statistics for Stochastic Processes : Estimation and Prediction / D. Bosq |
| Autore | Bosq, Denis |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | New York, : Springer, 1998 |
| Descrizione fisica | xvi, 210 p. ; 24 cm |
| Soggetto topico |
60-XX - Probability theory and stochastic processes [MSC 2020]
62-XX - Statistics [MSC 2020] 62G07 - Density estimation [MSC 2020] 62M09 - Non-Markovian processes: estimation [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] |
| Soggetto non controllato |
Kernels
Markov Processes Mixing Statistics Stochastic processes Variance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00298570 |
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Bosq, Denis
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| New York, : Springer, 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Nonparametric Statistics for Stochastic Processes : Estimation and Prediction / D. Bosq
| Nonparametric Statistics for Stochastic Processes : Estimation and Prediction / D. Bosq |
| Autore | Bosq, Denis |
| Pubbl/distr/stampa | New York, : Springer, 1996 |
| Descrizione fisica | xii, 169 p. ; 24 cm |
| Soggetto topico |
60-XX - Probability theory and stochastic processes [MSC 2020]
62-XX - Statistics [MSC 2020] 62G07 - Density estimation [MSC 2020] 62M09 - Non-Markovian processes: estimation [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] |
| Soggetto non controllato |
Kernels
Markov Processes Mixing Statistics Stochastic processes Variance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00297005 |
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Bosq, Denis
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| New York, : Springer, 1996 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Quasi-Likelihood And Its Application : A General Approach to Optimal Parameter Estimation / Christopher C. Heyde
| Quasi-Likelihood And Its Application : A General Approach to Optimal Parameter Estimation / Christopher C. Heyde |
| Autore | Heyde, Christopher C. |
| Pubbl/distr/stampa | New York, : Springer, 1997 |
| Descrizione fisica | ix, 235 p. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62F10 - Point estimation [MSC 2020] 62F12 - Asymptotic properties of parametric estimators [MSC 2020] 62M05 - Markov processes: estimation; hidden Markov models [MSC 2020] 62M09 - Non-Markovian processes: estimation [MSC 2020] |
| Soggetto non controllato |
Estimators
Generalized Linear Models Likelihood Mathematics Optimization Probability Statistics Stochastic processes Variance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00297860 |
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Heyde, Christopher C.
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| New York, : Springer, 1997 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Threshold Models in Non-linear Time Series Analysis / Howell Tong
| Threshold Models in Non-linear Time Series Analysis / Howell Tong |
| Autore | Tong, Howell |
| Pubbl/distr/stampa | New York, : Springer-Verlag, 1983 |
| Descrizione fisica | x, 323 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] 62M09 - Non-Markovian processes: estimation [MSC 2020] |
| Soggetto non controllato |
Ergodicity
Series Spectral Analysis Time Time Series Analysis Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0268634 |
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Tong, Howell
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| New York, : Springer-Verlag, 1983 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Threshold Models in Non-linear Time Series Analysis / Howell Tong
| Threshold Models in Non-linear Time Series Analysis / Howell Tong |
| Autore | Tong, Howell |
| Pubbl/distr/stampa | New York, : Springer-Verlag, 1983 |
| Descrizione fisica | x, 323 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62M09 - Non-Markovian processes: estimation [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] |
| Soggetto non controllato |
Ergodicity
Series Spectral analysis Time Time Series Analysis Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious. |
| Record Nr. | UNICAMPANIA-VAN00268634 |
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Tong, Howell
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| New York, : Springer-Verlag, 1983 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Time Series : Theory and Methods / Peter J. Brockwell, Richard A. Davis
| Time Series : Theory and Methods / Peter J. Brockwell, Richard A. Davis |
| Autore | Brockwell, Peter J. |
| Pubbl/distr/stampa | New York, : Springer-Verlag, 1987 |
| Descrizione fisica | xiv, 520 p. : ill. ; 24 cm |
| Altri autori (Persone) | Davis, Richard A. |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62M15 - Inference from stochastic processes and spectral analysis [MSC 2020] 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] 62M09 - Non-Markovian processes: estimation [MSC 2020] |
| Soggetto non controllato |
Estimator
Likelihood Statistics Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0268988 |
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Brockwell, Peter J.
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| New York, : Springer-Verlag, 1987 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Time Series : Theory and Methods / Peter J. Brockwell, Richard A. Davis
| Time Series : Theory and Methods / Peter J. Brockwell, Richard A. Davis |
| Autore | Brockwell, Peter J. |
| Pubbl/distr/stampa | New York, : Springer-Verlag, 1987 |
| Descrizione fisica | xiv, 520 p. : ill. ; 24 cm |
| Altri autori (Persone) | Davis, Richard A. |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62M09 - Non-Markovian processes: estimation [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62M15 - Inference from stochastic processes and spectral analysis [MSC 2020] 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] |
| Soggetto non controllato |
Estimator
Likelihood Statistics Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | We have attempted in this book to give a systematic account of linear time series models and their application to the modelling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It has been used both at the M. S. level, emphasizing the more practical aspects of modelling, and at the Ph. D. level, where the detailed mathematical derivations of the deeper results can be included. Distinctive features of the book are the extensive use of elementary Hilbert space methods and recursive prediction techniques based on innovations, use of the exact Gaussian likelihood and AIC for inference, a thorough treatment of the asymptotic behavior of the maximum likelihood estimators of the coefficients of univariate ARMA models, extensive illustrations of the tech niques by means of numerical examples, and a large number of problems for the reader. The companion diskette contains programs written for the IBM PC, which can be used to apply the methods described in the text. |
| Record Nr. | UNICAMPANIA-VAN00268988 |
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Brockwell, Peter J.
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| New York, : Springer-Verlag, 1987 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Soggetto topico
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62M09 - Non-Markovian processes: estimation [MSC 2020]
(9)
- 62-XX - Statistics [MSC 2020] (7)
- 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] (6)
- 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] (6)
- 60-XX - Probability theory and stochastic processes [MSC 2020] (3)