On trend estimation under monotone Gaussian subordination with long-memory: application to fossil pollen series

Menéndez, Patricia, Ghosh, Sucharita, Kuensch, Hans R. and Tinner, Willy (2013) On trend estimation under monotone Gaussian subordination with long-memory: application to fossil pollen series. Journal of Nonparametric Statistics, 25 4: 765-785. doi:10.1080/10485252.2013.826357


Author Menéndez, Patricia
Ghosh, Sucharita
Kuensch, Hans R.
Tinner, Willy
Title On trend estimation under monotone Gaussian subordination with long-memory: application to fossil pollen series
Journal name Journal of Nonparametric Statistics   Check publisher's open access policy
ISSN 1048-5252
1029-0311
1026-7654
Publication date 2013-12
Year available 2013
Sub-type Article (original research)
DOI 10.1080/10485252.2013.826357
Open Access Status
Volume 25
Issue 4
Start page 765
End page 785
Total pages 21
Place of publication Abingdon, Oxfordshire, United Kingdom
Publisher Taylor & Francis
Collection year 2014
Language eng
Formatted abstract
Fossil pollen data from stratigraphic cores are irregularly spaced in time due to non-linear age–depth relations. Moreover, their marginal distributions may vary over time. We address these features in a nonparametric regression model with errors that are monotone transformations of a latent continuous-time Gaussian process Z(T). Although Z(T) is unobserved, due to monotonicity, under suitable regularity conditions, it can be recovered facilitating further computations such as estimation of the long-memory parameter and the Hermite coefficients. The estimation of Z(T) itself involves estimation of the marginal distribution function of the regression errors. These issues are considered in proposing a plug-in algorithm for optimal bandwidth selection and construction of confidence bands for the trend function. Some high-resolution time series of pollen records from Lago di Origlio in Switzerland, which go back ca. 20,000 years are used to illustrate the methods.
Keyword Continuous time
Latent Gaussian process
Nonparametric regression
Palaeoclimate
Palaeoecology
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2014 Collection
 
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Created: Wed, 23 Oct 2013, 18:21:37 EST by Patricia Menendez Galvan on behalf of Mathematics