9783319213460 - model-free prediction and regression: a transformation-based approach to inference (frontiers in probability and the statistical sciences) von politis, dimitris n. (3 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2015
Serie: Buch 4 von 6 - Frontiers in Probability and the Statistical Sciences
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 104,43
EUR 14,02 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. In.
Sprache: Englisch
Verlag: Springer Verlag, 2015
Serie: Buch 4 von 6 - Frontiers in Probability and the Statistical Sciences
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 155,02
EUR 14,63 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Hardcover. Zustand: Brand New. 9.25x6.25x0.75 inches. In Stock.
Sprache: Englisch
Verlag: Palgrave Macmillan, 2015
Serie: Buch 4 von 6 - Frontiers in Probability and the Statistical Sciences
- Hardcover
Anbieter: Buchpark, Trebbin, DeutschlandBuchpark
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Gebraucht - Sehr gut
EUR 72,49
EUR 105,00 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 264 | Sprache: Englisch | Produktart: Bücher | 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 obse…rvable 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.
