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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
13 June 2017
|
| In: |
The annals of statistics
Year: 2017, Volume: 45, Issue: 3, Pages: 1312-1341 |
| ISSN: | 2168-8966 |
| DOI: | 10.1214/16-AOS1486 |
| Online Access: | 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 
|
| Author Notes: | Young K. Lee, Enno Mammen, Jens P. Nielsen, Byeong U. Park |
| Summary: | 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. |
|---|---|
| Item Description: | Gesehen am 29.01.2018 |
| Physical Description: | Online Resource |
| ISSN: | 2168-8966 |
| DOI: | 10.1214/16-AOS1486 |