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Model Free Prediction Regression Transformation Based by Politis Dimitris (25 results)
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Condition: As New. Unread book in perfect condition.
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Model-free Prediction and Regression : A Transformation-based Approach to Inference
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Model-free Prediction and Regression : A Transformation-based Approach to Inference
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Model-free Prediction and Regression : A Transformation-based Approach to Inference
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Model-free Prediction and Regression : A Transformation-based Approach to Inference
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Hardcover. Condition: Brand New. 9.25x6.25x0.75 inches. In Stock.
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Taschenbuch. Condition: Neu. Model-Free Prediction and Regression | A Transformation-Based Approach to Inference | Dimitris N. Politis | Taschenbuch | xvii | Englisch | 2016 | Springer | EAN 9783319352497 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Model-Free Prediction and Regression : A Transformation-Based Approach to Inference
Language: English
Published by Springer International Publishing, Springer Nature Switzerland, 2016
ISBN 10: 3319352490 ISBN 13: 9783319352497
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Model-Free Prediction and Regression : A Transformation-Based Approach to Inference
Language: English
Published by Springer International Publishing, Springer Nature Switzerland, 2015
ISBN 10: 3319213466 ISBN 13: 9783319213460
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 264 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Paperback. Condition: Brand New. reprint edition. 246 pages. 9.25x6.10x0.60 inches. In Stock.
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Condition: New. PRINT ON DEMAND pp. 190.
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Buch. Condition: Neu. Model-Free Prediction and Regression | A Transformation-Based Approach to Inference | Dimitris N. Politis | Buch | xvii | Englisch | 2015 | Springer | EAN 9783319213460 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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Condition: New. Print on Demand pp. 263.
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Condition: New. PRINT ON DEMAND pp. 263.
