NDLI: A multivariate prediction of spatial process with non-stationary covariance for Kuwait non-methane hydrocarbons levels

Content Provider	Springer Nature Link
Author	Al Awadhi, F. A.
Copyright Year	2009
Abstract	Predicting unmeasured realizations of multivariate spatial process responses is a fundamental problem in environmetrics. The study of levels of air pollutants is important for understanding and improving air quality in major urban areas. This research aims to handle the prediction in a Bayesian framework for non-methane hydrocarbons NMHC pollutant for the State of Kuwait where records of six monitor stations located in different sites are observed at successive time points. Our objective is to study the distribution level of NMHC with respect to time and metreological parameters and space and use this distribution to predict the concentration of NMHC in other sites of Kuwait using the minimum amount of data (reducing the cost). We will implement a hierarchical Bayesian approach assuming Gaussian random field technique that allows us to pool the data from different sites in predicting the exposure of the non-methane hydrocarbons in different regions of Kuwait.
Starting Page	57
Ending Page	77
Page Count	21
File Format	PDF
ISSN	13528505
Journal	Environmental and Ecological Statistics
Volume Number	18
Issue Number	1
e-ISSN	15733009
Language	English
Publisher	Springer US
Publisher Date	2009-08-08
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian statistics Gaussian random field Hierarchical model Spatial prediction Non-methane hydrocarbons Ecology Evolutionary Biology Mathematical Biology in General Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Environmental Science Statistics, Probability and Uncertainty

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