Operational time and in-sample density forecasting

• Verfasst von: Lee, Young K. [VerfasserIn]
• Verfasst von: Mammen, Enno [VerfasserIn]
• Verfasst von: Nielsen, Jens Perch [VerfasserIn]
• Verfasst von: Park, Byeong U. [VerfasserIn]
• Titel: Operational time and in-sample density forecasting
• Verf.angabe: Young K. Lee, Enno Mammen, Jens P. Nielsen, Byeong U. Park
• Umfang: 30 S.
• Fussnoten: Gesehen am 29.01.2018
• Titel Quelle: Enthalten in: The annals of statistics
• Jahr Quelle: 2017
• Band/Heft Quelle: 45(2017), 3, S. 1312-1341
• ISSN Quelle: 2168-8966
• DOI: doi:10.1214/16-AOS1486
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1567801870

Abstract

In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study.