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Analysis of Repeated Measures Data / M. Ataharul Islam, Rafiqul I. Chowdhury
| Analysis of Repeated Measures Data / M. Ataharul Islam, Rafiqul I. Chowdhury |
| Autore | Islam, M. Ataharul |
| Pubbl/distr/stampa | Singapore, : Springer, 2017 |
| Descrizione fisica | xix, 250 p. ; 24 cm |
| Altri autori (Persone) | Chowdhury, Rafiqul Islam |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62J12 - Generalized linear models (logistic models) [MSC 2020] 62G05 - Nonparametric estimation [MSC 2020] 62N01 - Censored data models [MSC 2020] 62H10 - Multivariate distribution of statistics [MSC 2020] 62M02 - Markov processes: hypothesis testing [MSC 2020] |
| Soggetto non controllato |
Bivariate Geometric Model
Generalized Linear Models Markov Chains Overdispersion Repeated Measures Data Weibull Data |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123582 |
Islam, M. Ataharul
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| Singapore, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Analysis of Repeated Measures Data / M. Ataharul Islam, Rafiqul I. Chowdhury
| Analysis of Repeated Measures Data / M. Ataharul Islam, Rafiqul I. Chowdhury |
| Autore | Islam, M. Ataharul |
| Pubbl/distr/stampa | Singapore, : Springer, 2017 |
| Descrizione fisica | xix, 250 p. ; 24 cm |
| Altri autori (Persone) | Chowdhury, Rafiqul Islam |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62G05 - Nonparametric estimation [MSC 2020] 62H10 - Multivariate distribution of statistics [MSC 2020] 62J12 - Generalized linear models (logistic models) [MSC 2020] 62M02 - Markov processes: hypothesis testing [MSC 2020] 62N01 - Censored data models [MSC 2020] |
| Soggetto non controllato |
Bivariate Geometric Model
Generalized Linear Models Markov Chains Overdispersion Repeated Measures Data Weibull Data |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00123582 |
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Islam, M. Ataharul
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| Singapore, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Analysis of Repeated Measures Data / M. Ataharul Islam, Rafiqul I. Chowdhury
| Analysis of Repeated Measures Data / M. Ataharul Islam, Rafiqul I. Chowdhury |
| Autore | Islam, M. Ataharul |
| Edizione | [Singapore : Springer, 2017] |
| Descrizione fisica | Pubblicazione in formato elettronico |
| Altri autori (Persone) | Chowdhury, Rafiqul Islam |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62J12 - Generalized linear models (logistic models) [MSC 2020] 62G05 - Nonparametric estimation [MSC 2020] 62N01 - Censored data models [MSC 2020] 62H10 - Multivariate distribution of statistics [MSC 2020] 62M02 - Markov processes: hypothesis testing [MSC 2020] |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-SUN0123582 |
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Islam, M. Ataharul
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| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Model-free prediction and regression : a transformation-based approach to inference / Dimitris N. Politis
| Model-free prediction and regression : a transformation-based approach to inference / Dimitris N. Politis |
| Autore | Politis, Dimitris N. |
| Pubbl/distr/stampa | [Cham], : Springer, 2015 |
| Descrizione fisica | XVII, 246 p. ; 24 cm |
| Soggetto topico |
62J02 - General nonlinear regression [MSC 2020]
62-XX - Statistics [MSC 2020] 62J05 - Linear regression; mixed models [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62G08 - Nonparametric regression and quantile regression [MSC 2020] 62M02 - Markov processes: hypothesis testing [MSC 2020] |
| Soggetto non controllato |
Independent and identically distributed
Markov Processes Modeling Prediction Regression Statistical interference Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0113693 |
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Politis, Dimitris N.
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| [Cham], : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Model-free prediction and regression : a transformation-based approach to inference / Dimitris N. Politis
| Model-free prediction and regression : a transformation-based approach to inference / Dimitris N. Politis |
| Autore | Politis, Dimitris N. |
| Pubbl/distr/stampa | [Cham], : Springer, 2015 |
| Descrizione fisica | XVII, 246 p. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62G08 - Nonparametric regression and quantile regression [MSC 2020] 62J02 - General nonlinear regression [MSC 2020] 62J05 - Linear regression; mixed models [MSC 2020] 62M02 - Markov processes: hypothesis testing [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] |
| Soggetto non controllato |
Independent and identically distributed
Markov Processes Modeling Prediction Regression Statistical interference Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference. |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00113693 |
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Politis, Dimitris N.
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| [Cham], : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Model-free prediction and regression : a transformation-based approach to inference / Dimitris N. Politis
| Model-free prediction and regression : a transformation-based approach to inference / Dimitris N. Politis |
| Autore | Politis, Dimitris N. |
| Edizione | [[Cham] : Springer, 2015] |
| Descrizione fisica | Pubblicazione in formato elettronico |
| Soggetto topico |
62J02 - General nonlinear regression [MSC 2020]
62-XX - Statistics [MSC 2020] 62J05 - Linear regression; mixed models [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62G08 - Nonparametric regression and quantile regression [MSC 2020] 62M02 - Markov processes: hypothesis testing [MSC 2020] |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-SUN0113693 |
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Politis, Dimitris N.
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| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Statistical Models Based on Counting Processes / Per Kragh Andersen ... [et al.]
| Statistical Models Based on Counting Processes / Per Kragh Andersen ... [et al.] |
| Pubbl/distr/stampa | New York, : Springer, 1993 |
| Descrizione fisica | xi, 767 p. : ill. ; 25 cm |
| Soggetto topico |
62M02 - Markov processes: hypothesis testing [MSC 2020]
62M05 - Markov processes: estimation; hidden Markov models [MSC 2020] 62Mxx - Inference from stochastic processes [MSC 2020] |
| Soggetto non controllato |
Censoring
Estimator Likelihood Survival analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00292017 |
| New York, : Springer, 1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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