{"title":"Other titles from iSTE in Innovation, Entrepreneurship and Management","authors":"","doi":"10.1002/9781119721611.oth","DOIUrl":"https://doi.org/10.1002/9781119721611.oth","url":null,"abstract":"","PeriodicalId":378326,"journal":{"name":"Data Analysis and Applications 4","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128681275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"How to Model the Covariance Structure in a Spatial Framework: Variogram or Correlation Function?","authors":"Giovanni Pistone, G. Vicario","doi":"10.1002/9781119721611.ch10","DOIUrl":"https://doi.org/10.1002/9781119721611.ch10","url":null,"abstract":"The basic Kriging's model assumes a Gaussian distribution with stationary mean and stationary variance. In such a setting, the joint distribution of the spatial process is characterized by the common variance and the correlation matrix or, equivalently, by the common variance and the variogram matrix. We discuss in in detail the option to actually use the variogram as a parameterization.","PeriodicalId":378326,"journal":{"name":"Data Analysis and Applications 4","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121164269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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