A Novel Approach to Finding Sampling Distributions for Truncated Laws Via Unbiasedness Equivalence Principle
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 2-1, March 2016, Pages: 40-48
Received: Jan. 26, 2016;
Accepted: Jan. 28, 2016;
Published: Feb. 23, 2016
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Nicholas A. Nechval, Department of Mathematics, Baltic International Academy, Riga, Latvia
Sergey Prisyazhnyuk, Department of Geoinformation Systems, National Research University of Information Technologies, Mechanics and Optics, St-Petersburg, Russia
Vladimir F. Strelchonok, Department of Mathematics, Baltic International Academy, Riga, Latvia
Truncated distributions arise naturally in many practical situations. In this paper, the problem of finding sampling distributions for truncated laws is considered. This problem concerns the very important area of information processing in Industrial Engineering. It remains today perhaps the most difficult and important of all the problems of mathematical statistics that require considerable efforts and great skill for investigation. In a given problem, most would prefer to find a sampling distribution for truncated law by the simplest method available. For many situations encountered in textbooks and in the literature, the approach discussed here is simple and straightforward. It is based on use of the unbiasedness equivalence principle (UEP) that represents a new idea which often allows one to provide a neat method for finding sampling distributions for truncated laws. It avoids explicit integration over the sample space and the attendant Jacobian but at the expense of verifying completeness of the recognized family of densities. Fortunately, general results on completeness obviate the need for this verification in many problems involving exponential families. The proposed approach allows one to obtain results for truncated laws via the results obtained for non-truncated laws. It is much simpler than the known approaches. In many situations this approach allows one to find the results for truncated laws with known truncation points and to estimate system reliability in a simple way. The approach can also be used to find the sampling distribution for truncated law when some or all of its truncation parameters are left unspecified. The illustrative examples are given.
Nicholas A. Nechval,
Vladimir F. Strelchonok,
A Novel Approach to Finding Sampling Distributions for Truncated Laws Via Unbiasedness Equivalence Principle, American Journal of Theoretical and Applied Statistics. Special Issue: Novel Ideas for Efficient Optimization of Statistical Decisions and Predictive Inferences under Parametric Uncertainty of Underlying Models with Applications.
Vol. 5, No. 2-1,
2016, pp. 40-48.
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