Neyman Measurement and Prediction Procedures

Elart von Collani, Dumitrescu Monica and Lepenis Rouven
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Elart von Collani: University of Würzburg, Sanderring 2, D-97070 Würzburg, Germany
Dumitrescu Monica: University of Bucharest, Faculty of Mathematics, Str. Academiei 14, 70 109 Bucharest, Romania
Lepenis Rouven: BMW AG, Moosacherstr. 23, D-80809 München, Germany

Stochastics and Quality Control, 2001, vol. 16, issue 1, 109-132

Abstract: Consider the following two situations which are characteristic for the application of statistical procedures.1.)There is a deterministic variable of interest , whose actual value θ∈ Θ is unknown.2.)There is a random variable of interest with uncertain outcome .The aim in the first situation is to determine the unknown actual value θ of the quantity of interest . In the second situation the aim is to make a prediction about the uncertain outcome of the random variable of interest . Thus, measurement procedure and a prediction procedures are needed.In this paper it is shown that appropriately defined the two problems are equivalent in the sense that any solution of the one problem implies a solution of the second problem. Moreover, a quality indicator is derived reflecting the degree of “fitness for use” of a given procedure and enabling definition and determination of “optimal” statistical procedures.In agreement with general scientific practice, but in contrast to traditional statistics, the measurement and prediction procedures are developed for a suitably specified bounded range for the admissible values of the deterministic variable .

Keywords: Measurement Procedure; Prediction Procedure; Measurement & Prediction Space; Quality Indicator; Procedure's Reliability (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1515/EQC.2001.109

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