| ESTIMATION IN COMPOSITIONAL DATA ANALYSIS |
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| 作者姓名： | WILLIAMS.RAYENS CIDAMBISRINIVASAN |
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| 作者单位： | Department of Statistics University of Kentucky，Lexington，Ky 40506，U.S.A.，Department of Statistics，University of Kentucky，Lexington，Ky 40506，U.S.A. |
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| 摘 要： | Compositional data arise naturally in several branches of science,including chemistry,geology,biology,medicine,ecology and manufacturing design.In chemistry,these constrained data seem to occur typicallywhen raw data are normalized or when output is obtained from a constrained estimation procedure,suchas might be used in a source apportionment problem.It is important not only for chemists to be awarethat the usual multivariate statistical techniques are not applicable to constrained data,but also to haveaccess to appropriate techniques as they become available.The currently available methodology is dueprincipally to Aitchison and is based on log-normal models.This paper suggests new parametric andnon-parametric approaches to significantly improve the existing methodology.In the parametric setting,some recent work of Rayens and Srinivasan is extended and a practical regression model is proposed.In the development of the non-parametric approach,minimum distance methods coupled withmultivariate bootstrap techniques are used to obtain point and region estimators.
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ESTIMATION IN COMPOSITIONAL DATA ANALYSIS |
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| WILLIAMS.RAYENS,CIDAMBISRINIVASAN.ESTIMATION IN COMPOSITIONAL DATA ANALYSIS[J].Journal of Geographical Sciences,1991(4). |
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| Authors: | WILLIAM SRAYENS CIDAMBI SRINIVASAN |
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| Institution: | WILLIAM S.RAYENS CIDAMBI SRINIVASAN Department of Statistics,University of Kentucky,Lexington,Ky,U.S.A. |
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| Abstract: | Compositional data arise naturally in several branches of science,including chemistry,geology,biology, medicine,ecology and manufacturing design.In chemistry,these constrained data seem to occur typically when raw data are normalized or when output is obtained from a constrained estimation procedure,such as might be used in a source apportionment problem.It is important not only for chemists to be aware that the usual multivariate statistical techniques are not applicable to constrained data,but also to have access to appropriate techniques as they become available.The currently available methodology is due principally to Aitchison and is based on log-normal models.This paper suggests new parametric and non-parametric approaches to significantly improve the existing methodology.In the parametric setting, some recent work of Rayens and Srinivasan is extended and a practical regression model is proposed. In the development of the non-parametric approach,minimum distance methods coupled with multivariate bootstrap techniques are used to obtain point and region estimators. |
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| Keywords: | Closure Normalization Multivariate trimming Minimum distance Bootstrap |
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