Marginally parameterized spatio-temporal models and stepwise maximum likelihood estimation

Matthew Edwards, Stefano Castruccio and Dorit Hammerling

Computational Statistics & Data Analysis, 2020, vol. 151, issue C

Abstract: In order to learn the complex features of large spatio-temporal data, models with a large number of parameters are often required. However, inference is often infeasible due to the computational and memory costs of maximum likelihood estimation (MLE). The class of marginally parameterized (MP) models is introduced, where estimation can be performed efficiently with a sequence of marginal likelihood functions with stepwise maximum likelihood estimation (SMLE). The conditions under which the stepwise estimators are consistent are provided, and it is shown that this class of models includes the diagonal vector autoregressive moving average model. It is demonstrated that the parameters of this model can be obtained at least three orders of magnitude faster with SMLE compared to MLE, with only a small loss in statistical efficiency. A MP model is applied to a spatio-temporal global climate data set consisting of over five million data points, and it is demonstrated how estimation can be achieved in less than one hour on a laptop with a dual core at 2.9 Ghz.

Keywords: Spatio-temporal model; Massive data; Stepwise estimation; Parallel computation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947320301092
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:151:y:2020:i:c:s0167947320301092

DOI: 10.1016/j.csda.2020.107018

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().