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Analytical Methods in Statistics : AMISTAT, Liberec, Czech Republic, September 2019 / Matúš Maciak, Michal Pešta, Martin Schindler editors
| Analytical Methods in Statistics : AMISTAT, Liberec, Czech Republic, September 2019 / Matúš Maciak, Michal Pešta, Martin Schindler editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | x, 156 p. : ill. ; 24 cm |
| Soggetto topico |
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020]
62-XX - Statistics [MSC 2020] 62G07 - Density estimation [MSC 2020] 62M45 - Neural nets and related approaches to inference from stochastic processes [MSC 2020] 62H15 - Hypothesis testing in multivariate analysis [MSC 2020] |
| Soggetto non controllato |
(auto)regression
Analytical methods Asymptotics Estimation Fisher information Hypothesis Testing Meta learning Neural networks Robustness Statistical Methods Stochastic inequalities Stochastic models |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0248705 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Analytical Methods in Statistics : AMISTAT, Liberec, Czech Republic, September 2019 / Matúš Maciak, Michal Pešta, Martin Schindler editors
| Analytical Methods in Statistics : AMISTAT, Liberec, Czech Republic, September 2019 / Matúš Maciak, Michal Pešta, Martin Schindler editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | x, 156 p. : ill. ; 24 cm |
| Soggetto topico |
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020]
62-XX - Statistics [MSC 2020] 62G07 - Density estimation [MSC 2020] 62H15 - Hypothesis testing in multivariate analysis [MSC 2020] 62M45 - Neural nets and related approaches to inference from stochastic processes [MSC 2020] |
| Soggetto non controllato |
(auto)regression
Analytical methods Asymptotics Estimation Fisher information Hypothesis Testing Meta learning Neural networks Robustness Statistical Methods Stochastic inequalities Stochastic models |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00248705 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Non-Gaussian Autoregressive-Type Time Series / N. Balakrishna
| Non-Gaussian Autoregressive-Type Time Series / N. Balakrishna |
| Autore | Balakrishna, Narayana |
| Pubbl/distr/stampa | Singapore, : Springer, 2021 |
| Descrizione fisica | xviii, 225 p. : ill. ; 24 cm |
| Soggetto topico |
62F10 - Point estimation [MSC 2020]
62-XX - Statistics [MSC 2020] 62J05 - Linear regression; mixed models [MSC 2020] 62J12 - Generalized linear models (logistic models) [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62F12 - Asymptotic properties of parametric estimators [MSC 2020] |
| Soggetto non controllato |
(auto)regression
Autoregressive models with non Gaussian innovations Autoregressive models with stable innovations Cauchy autoregressive models Estimating function methods Exponential autoregressive models Gamma autoregressive models Laplace autoregressive models Logistic autoregressive models Maximum probability estimators Minification models Mixture autoregressive models Non Gaussian time series Product autoregressive models Quasi likelihood methods Time series models with slowly varying innovations |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0275481 |
Balakrishna, Narayana
|
||
| Singapore, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Non-Gaussian Autoregressive-Type Time Series / N. Balakrishna
| Non-Gaussian Autoregressive-Type Time Series / N. Balakrishna |
| Autore | Balakrishna, Narayana |
| Pubbl/distr/stampa | Singapore, : Springer, 2021 |
| Descrizione fisica | xviii, 225 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62F10 - Point estimation [MSC 2020] 62F12 - Asymptotic properties of parametric estimators [MSC 2020] 62J05 - Linear regression; mixed models [MSC 2020] 62J12 - Generalized linear models (logistic models) [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] |
| Soggetto non controllato |
(auto)regression
Autoregressive models with non Gaussian innovations Autoregressive models with stable innovations Cauchy autoregressive models Estimating function methods Exponential autoregressive models Gamma autoregressive models Laplace autoregressive models Logistic autoregressive models Maximum probability estimators Minification models Mixture autoregressive models Non Gaussian time series Product autoregressive models Quasi likelihood methods Time series models with slowly varying innovations |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00275481 |
|
Balakrishna, Narayana
|
||
| Singapore, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||