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Central Mean Subspace in Time Series.
- Source :
-
Journal of Computational & Graphical Statistics . Sep2009, Vol. 18 Issue 3, p717-730. 14p. 5 Charts, 2 Graphs. - Publication Year :
- 2009
-
Abstract
- We propose a notion of central mean dimension reduction subspace for time series {x1} which does not require specification of a model but seeks to find a p x d matrix Φd, d ≤ p, so that the d x 1 vector where ΦdTXt-1 where Xt-1= (xt-1,…Xt-p)T for some p ≥ 1, includes all the information about xt that is available from E(xt∣Xt-1). For known p and d, we estimate the mean central subspace through the Nadaraya-Watson kernel smoother and establish the strong consistency of our estimator. In addition, we propose estimation of d and p using a modified Schwarz Bayesian criterion, if either of d and p is unknown. Finally, we examine the performance of all the estimators extensively through a variety of simulations and provide a new analysis of the well-known Canadian lynx data. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10618600
- Volume :
- 18
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of Computational & Graphical Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 44721163
- Full Text :
- https://doi.org/10.1198/jcgs.2009.08076