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Neyman Measurement and Prediction Procedures

Author

Listed:
  • von Collani Elart

    (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)

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 .

Suggested Citation

  • von Collani Elart & Dumitrescu Monica & Lepenis Rouven, 2001. "Neyman Measurement and Prediction Procedures," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 109-132, January.
    • Handle: RePEc:bpj:ecqcon:v:16:y:2001:i:1:p:109-132:n:1
    DOI: 10.1515/EQC.2001.109
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    References listed on IDEAS

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    1. Neyman, Jerzy, 1952. "Lectures and Conferences on Mathematical Statistics and Probability," USDA Miscellaneous 327287, United States Department of Agriculture.
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