| CALIBRATION:REGRESSION MODELS FOR CONCENTRATION PROBLEMS |
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| 作者姓名： | H.J.RUSKIN |
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| 作者单位： | School of |
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| 摘 要： | A brief review of the literature on point estimators in linear calibration problems is undertaken,Supportive evidence for the relative merits of the classical and inverse regression models,drawn in generalfrom the classical inferential and Bayesian approaches,is considered and the criteria for comparison ofthe estimators are discussed with respect to their suitability for certain classes of problems.Theperformance of the estimators is assessed with respect to determining the current value of ‘x’,thepercentage concentration of administered drug levels in blood in this example.No single‘best’methodof estimation appears to hold for all values of the unknown concentration when performance is assessedby criteria based on the mean square error (MSE).However,the inverse estimator would appear to besuperior to the classical for those values of unknown X close to x.
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CALIBRATION:REGRESSION MODELS FOR CONCENTRATION PROBLEMS |
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| H.J.RUSKIN.CALIBRATION:REGRESSION MODELS FOR CONCENTRATION PROBLEMS[J].Journal of Geographical Sciences,1989(2). |
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| Authors: | HJRUSKIN School of Computer Applications National Institute for Higher Education Collins Avenue Glasnevin Dublin Eire |
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| Abstract: | |
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| Keywords: | Calibration Inverse estimator Classical estimator Mean square error(MSE) |
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