{"title":"\"ADAPTIVE PARAMETRIC MODEL FOR NONSTATIONARY SPATIAL COVARIANCE\"","authors":"J. T. Eghwerido, J. I. Mbegbu","doi":"10.22452/mjs.vol39no2.4","DOIUrl":null,"url":null,"abstract":"In modelling environment processes, multi-disciplinary methods are used to explain, explore and predict how the earth responds to natural human-induced environmental changes over time. Consequently, when analyzing spatial processes in environmental and ecological studies, the spatial parameters of interest are always heterogeneous. This often negates the stationarity assumption. In this article, we propose the adaptive parametric nonstationary covariance structure for spatial processes. The adaptive turning parameter for this model was also proposed for nonstationary processes. The flexibility and efficiency of the propose model was examined through simulation. A real life data was also use to examine the efficiency of the propose model. The results show that the propose model perform competitively with existing models.","PeriodicalId":18094,"journal":{"name":"Malaysian journal of science","volume":"39 1","pages":"51-70"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Malaysian journal of science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22452/mjs.vol39no2.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Multidisciplinary","Score":null,"Total":0}
引用次数: 1
Abstract
In modelling environment processes, multi-disciplinary methods are used to explain, explore and predict how the earth responds to natural human-induced environmental changes over time. Consequently, when analyzing spatial processes in environmental and ecological studies, the spatial parameters of interest are always heterogeneous. This often negates the stationarity assumption. In this article, we propose the adaptive parametric nonstationary covariance structure for spatial processes. The adaptive turning parameter for this model was also proposed for nonstationary processes. The flexibility and efficiency of the propose model was examined through simulation. A real life data was also use to examine the efficiency of the propose model. The results show that the propose model perform competitively with existing models.