000			02991nam a2200313| 4500
001			83990
005			20220110132118.0
010			_a978-3-319-21347-7 _dcompra
090			_a83990
100			_a20190128d2015 k||y0pory50 ba
101			_aeng
102			_aCH
200			_aModel-free prediction and regression _bDocumento eletrn̤ico _ea transformation-based approach to inference _fDimitris N. Politis
210			_aCham _cSpringer International Publishing _d2015
215			_aXVII, 246 p. _cil.
225			_aFrontiers in Probability and the Statistical Sciences
300			_aColocaȯ̂: Online
303			_aThe 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.
410			_x2624-9987
606			_91379 _aEstats̕tica matemt̀ica
606			_93627 _aEstats̕tica
680			_aQA276
700			_933443 _aPolitis _bDimitris N.
801			_gRPC _aPT
856			_uhttps://doi.org/10.1007/978-3-319-21347-7
942			_2lcc _cF _n0