Dynamic Models for Spatiotemporal Data

Stroud, Jonathan; Müller, Peter; Sansó, Bruno

doi:10.1111/1467-9868.00305

Cited by 175 publications

(107 citation statements)

References 28 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…In temporally changing systems, the simplicity of this model makes it usable in a wide range of situations. While other models exist to interpret nonstationary covariance structures, the complexity of such models is discouraging for many potential uses [Stroud et al, 1999]. The model developed through this research can be applied to any watershed system, easily but accurately representing temporal changes in covariance structures.…”

Section: Discussionmentioning

confidence: 99%

“…However, the majority of such studies assume the covariance structures are temporally stationary or result in highly complex methods for evaluating the temporal component [Stroud et al, 1999]. It is important to consider that covariance structures may change temporally and equally as important to identify general, simple methods for examining this temporal nonstationarity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial and temporal stream temperature prediction: Modeling nonstationary temporal covariance structures

Gardner

Sullivan

2004

Water Resources Research

View full text Add to dashboard Cite

[1] Stream temperature is a measure of water quality and directly influences both the biotic and abiotic dynamics within the aquatic system. Because of its importance, there is a need to find better methods of monitoring and modeling stream temperatures. Gardner et al. [2003] derived a networked geostatistical model to explain the spatial patterns of stream temperatures based on 72 temperature loggers recording stream temperatures hourly throughout the Beaverkill watershed network, New York, for a single time period. Because temperatures and temperature relationships between stream sections change over the season, the covariance structure of the system is likely also to change with time. The covariance structure of stream temperature data collected throughout the month of July was examined and found to be nonstationary temporally. However, the observed changes in the covariance structure were found to be highly dependent on main stem stream temperatures over time. Five nested correlation models were created and compared using Mallow's C p . One model representing large-scale variation in the sill as a linear function of the main stem temperature for each day was selected as the most parsimonious model.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial and temporal stream temperature prediction: Modeling nonstationary temporal covariance structures

Gardner

Sullivan

2004

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Since 2000 alone, they have been adopted in mixture hazard models (Louzada-Neto et al 2002), spatio-temporal models (Stroud et al 2001), structural equation models (Zhu and Lee 2001), disease mapping (Green and Richardson 2002), analysis of proportions (Brooks 2001), correlated data and clustered models (Chib and Hamilton 2000, Dunson 2000, Chen and Dey 2000, classification and discrimination (Wruck et al 2001), experimental design and analysis (Nobile andGreen 2000, Sebastiani andWynn 2000), random effects generalised linear models (Lenk and DeSarbo 2000) and binary data (Basu and Mukhopadhyay 2000). Mixtures of Weibulls (Tsionas 2002) and Gammas (Wiper et al 2001) have been considered, along with computational issues associated with MCMC methods (Liang and Wong 2001), issues of convergence (Liang and Wong 2001), the display…”

Section: Extensions To the Mixture Frameworkmentioning

confidence: 99%

Bayesian Modelling and Inference on Mixtures of Distributions

Marin¹,

Mengersen²,

Robert³

2005

Handbook of Statistics

330

364

View full text Add to dashboard Cite

'But, as you have already pointed out, we do not need any more disjointed clues,' said Bartholomew. 'That has been our problem all along: we have a mass of small facts and small scraps of information, but we are unable to make any sense out of them. The last thing we need is more.' Susanna Gregory, A Summer of Discontent IntroductionToday's data analysts and modellers are in the luxurious position of being able to more closely describe, estimate, predict and infer about complex systems of interest, thanks to ever more powerful computational methods but also wider ranges of modelling distributions. Mixture models constitute a fascinating illustration of these aspects: while within a parametric family, they offer malleable approximations in non-parametric settings; although based on standard distributions, they pose highly complex computational challenges; and they are both easy to constrain to meet identifiability requirements and fall within the class of ill-posed problems. They also provide an endless benchmark for assessing new techniques, from the EM algorithm to reversible jump methodology. In particular, they exemplify the

show abstract

“…This can also be achieved by Kriging techniques, such as local space-time Kriging (Gething et al 2007), or by dynamic models for non-stationary spatio-temporal data (Stroud et al 2001). As an alternative to standard Kriging procedures, we consider a dynamic semiparametric factor model (DSFM) which has recently been suggested by Song et al (2010Song et al ( , 2013 for analyzing high dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Modelling spatio-temporal variability of temperature

et al. 2015

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractForecasting temperature in time and space is an important precondition for both the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insurance. In this article, we show how this task can be accomplished by means of Kriging techniques. Moreover, we compare Kriging with a dynamic semiparametric factor model (DSFM) that has been recently developed for the analysis of high dimensional financial data. We apply both methods to comprehensive temperature data covering a large area of China and assess their performance in terms of predicting a temperature index at an unobserved location. The results show that the DSFM performs worse than standard Kriging techniques. Moreover, we show how geographic basis risk inherent to weather derivatives can be mitigated by regional diversification.

show abstract

Dynamic Models for Spatiotemporal Data

Cited by 175 publications

References 28 publications

Spatial and temporal stream temperature prediction: Modeling nonstationary temporal covariance structures

Spatial and temporal stream temperature prediction: Modeling nonstationary temporal covariance structures

Bayesian Modelling and Inference on Mixtures of Distributions

Modelling spatio-temporal variability of temperature

Contact Info

Product

Resources

About