Model-Free Prediction and Regression

Lingua: inglese

Editore: Springer, Palgrave Macmillan Aug 2016, 2016

3319352490 / 9783319352497

Serie: Libro 4 di 6 - Frontiers in Probability and the Statistical Sciences

Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 264 pp. Englisch. …

Codice articolo 9783319352497

Titolo
Model-Free Prediction and Regression
Autore
Dimitris N. Politis
Editore
Springer, Palgrave Macmillan Aug 2016
Anno di pubblicazione
2016
Condizione
Neu
Rilegatura
Taschenbuch
Lingua
inglese
ISBN 10
3319352490
ISBN 13
9783319352497
Peso dell'articolo
406 grammi
Dimensioni
235x155x15 mm
Serie
Libro 4 di 6: Frontiers in Probability and the Statistical Sciences

buchversandmimpf2000

Emtmannsberg, BAYE, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 23 gennaio 2017

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 60 a 60 giorni lavorativiDa 60 a 60 giorni lavorativi
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