LEADER 04711nam 22006495 450 001 9910300242803321 005 20250312212742.0 010 $a9783319213477 010 $a3319213474 024 7 $a10.1007/978-3-319-21347-7 035 $a(CKB)3710000000515632 035 $a(EBL)4092955 035 $a(SSID)ssj0001585330 035 $a(PQKBManifestationID)16264914 035 $a(PQKBTitleCode)TC0001585330 035 $a(PQKBWorkID)14865489 035 $a(PQKB)10036982 035 $a(DE-He213)978-3-319-21347-7 035 $a(MiAaPQ)EBC4092955 035 $a(PPN)190526858 035 $a(EXLCZ)993710000000515632 100 $a20151113d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModel-Free Prediction and Regression $eA Transformation-Based Approach to Inference /$fby Dimitris N. Politis 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (256 p.) 225 1 $aFrontiers in Probability and the Statistical Sciences,$x2624-9995 300 $aDescription based upon print version of record. 311 08$a9783319213460 311 08$a3319213466 320 $aIncludes bibliographical references. 327 $aPrediction: some heuristic notions -- The Model-free Prediction Principle -- Model-based prediction in regression -- Model-free prediction in regression -- Model-free vs. model-based confidence intervals -- Linear time series and optimal linear prediction -- Model-based prediction in autoregression -- Model-free inference for Markov processes -- Predictive inference for locally stationary time series -- Model-free vs. model-based volatility prediction. 330 $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 theadvent 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 0$aFrontiers in Probability and the Statistical Sciences,$x2624-9995 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aStatistics in Business, Management, Economics, Finance, Insurance 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 676 $a519.5 700 $aPolitis$b Dimitris N$4aut$4http://id.loc.gov/vocabulary/relators/aut$0254529 906 $aBOOK 912 $a9910300242803321 996 $aModel-free prediction and regression$91496943 997 $aUNINA