Operational time and in-sample density forecasting
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 assump...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 June 2017
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| In: |
The annals of statistics
Year: 2017, Jahrgang: 45, Heft: 3, Pages: 1312-1341 |
| ISSN: | 2168-8966 |
| DOI: | 10.1214/16-AOS1486 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1214/16-AOS1486 Verlag, kostenfrei, Volltext: https://projecteuclid.org/euclid.aos/1497319696 Verlag, kostenfrei, Volltext: https://projecteuclid.org/download/pdfview_1/euclid.aos/1497319696 
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| Verfasserangaben: | Young K. Lee, Enno Mammen, Jens P. Nielsen, Byeong U. Park |
| Zusammenfassung: | 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. |
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| Beschreibung: | Gesehen am 29.01.2018 |
| Beschreibung: | Online Resource |
| ISSN: | 2168-8966 |
| DOI: | 10.1214/16-AOS1486 |