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

  • Submission Deadline: Jan. 30, 2016
  • Status: Submission Closed
  • Lead Guest Editor: Nicholas A. Nechval
About This Special Issue
The aim of this issue is to promote the novel ideas for efficient optimization of statistical decisions and predictive inferences under parametric uncertainty of underlying models with applications. It is expected that these ideas give interesting and novel contributions to statistical theory and its applications at a good mathematical level, where the theoretical results are obtained via the frequentist (non-Bayesian) statistical approach. Frequentist probability interpretations of the methods considered are clear. Bayesian methods are not considered here. It will be noted, however, that although subjective Bayesian approach has a clear personal probability interpretation, it is not generally clear how this should be applied to non-personal prediction or decisions. Objective Bayesian methods, on the other hand, do not have clear probability interpretations in finite samples. Since genuinely useful applications remain rare, this issue focuses on the practice of applying the ideas presented here to solve efficiently real problems with numerical results on the relative efficiency of the proposed method (as compared with the known methods) and examples for the applicability of the theoretical results. It is assumed that the efficient optimization take into account statistical information, which is contained in the past, previous, or current data samples, as completely as possible to allow one to find efficient decision rules and predictive inferences. This special issue has to provide academicians and young researchers worldwide high quality peer-reviewed research articles, covering the topics of primary interest, and to bring together mathematicians’ papers from different aspects of efficient optimization of statistical decisions and predictive inferences (under parametric uncertainty of underlying models with applications) as well as to present different points of views and methods.

The topics covered by the special issue include (but are not limited to):

1. Diagnostics
2. Signal Processing
3. Transportation Processes
4. Dual Control
5. Pattern Recognition
6. Reliability
7. Quality Control
8. Inventory Control
9. Industrial Engineering
10. Planning In-Service Inspections
11. Acceptance Testing
12. Prediction
13. Statistical Decisions in Medicine
14. Statistical Decisions in Remote Sensing
Lead Guest Editor
  • Nicholas A. Nechval

    Department of Mathematics, Baltic International Academy, Riga, Latvia

Guest Editors
  • Vladimir F. Strelchonok

    Department of Mathematics, Baltic International Academy, Riga, Latvia

  • Vijay Kumar

    Department of Mathematics, Manav Rachna International University, Faridabad, India

Published Articles
  • A Novel Approach to Finding Sampling Distributions for Truncated Laws Via Unbiasedness Equivalence Principle

    Nicholas A. Nechval , Sergey Prisyazhnyuk , Vladimir F. Strelchonok

    Issue: Volume 5, Issue 2-1, March 2016
    Pages: 40-48
    Received: Jan. 26, 2016
    Accepted: Jan. 28, 2016
    Published: Feb. 23, 2016
    DOI: 10.11648/j.ajtas.s.2016050201.16
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    Abstract: 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 mathe... Show More
  • Efficient Predictive Inferences for Future Outcomes Under Parametric Uncertainty of Underlying Models

    Nicholas A. Nechval , Natalija Ribakova , Gundars Berzins

    Issue: Volume 5, Issue 2-1, March 2016
    Pages: 49-55
    Received: Jan. 31, 2016
    Accepted: Feb. 02, 2016
    Published: Feb. 23, 2016
    DOI: 10.11648/j.ajtas.s.2016050201.17
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    Abstract: Predictive inferences (predictive distributions, prediction and tolerance limits) for future outcomes on the basis of the past and present knowledge represent a fundamental problem of statistics, arising in many contexts and producing varied solutions. In this paper, new-sample prediction based on a previous sample (i.e., when for predicting the fu... Show More
  • Innovative Planning in-Service Inspections of Fatigued Structures Under Parametric Uncertainty of Lifetime Models

    Nicholas A. Nechval , Vadims Danovics , Natalija Ribakova

    Issue: Volume 5, Issue 2-1, March 2016
    Pages: 29-39
    Received: Jan. 17, 2016
    Accepted: Jan. 19, 2016
    Published: Feb. 04, 2016
    DOI: 10.11648/j.ajtas.s.2016050201.15
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    Abstract: The main aim of this paper is to present more accurate stochastic fatigue models for solving the fatigue reliability problems, which are attractively simple and easy to apply in practice for situations where it is difficult to quantify the costs associated with inspections and undetected cracks. From an engineering standpoint the fatigue life of a ... Show More
  • Optimization of Statistical Decisions for Age Replacement Problems via a New Pivotal Quantity Averaging Approach

    Nicholas A. Nechval , Gundars Berzins , Vadims Danovics

    Issue: Volume 5, Issue 2-1, March 2016
    Pages: 21-28
    Received: Jan. 07, 2016
    Accepted: Jan. 08, 2016
    Published: Jan. 27, 2016
    DOI: 10.11648/j.ajtas.s.2016050201.14
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    Abstract: Age replacement strategies, where a unit is replaced upon failure or on reaching a predetermined age, whichever occurs first, provide simple and intuitively attractive replacement guidelines for technical units. Within theory of stochastic processes, the optimal preventive replacement age, in the sense of leading to minimal expected costs per unit ... Show More
  • A New Approach to Dose Estimation in Drug Development Based on Maximization of Likelihood of Grouped Data

    Nicholas A. Nechval , Gundars Berzins , Vadims Danovics

    Issue: Volume 5, Issue 2-1, March 2016
    Pages: 12-20
    Received: Nov. 02, 2015
    Accepted: Nov. 02, 2015
    Published: Nov. 30, 2015
    DOI: 10.11648/j.ajtas.s.2016050201.13
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    Abstract: Identifying the ‘right’ dose is one of the most critical and difficult steps in the clinical development process of any medicinal drug. Its importance cannot be understated: selecting too high a dose can result in unacceptable toxicity and associated safety problems, while choosing too low a dose leads to smaller chances of showing sufficient effic... Show More
  • Efficient Approach to Pattern Recognition Based on Minimization of Misclassification Probability

    Nicholas A. Nechval , Konstantin N. Nechval

    Issue: Volume 5, Issue 2-1, March 2016
    Pages: 7-11
    Received: Sep. 09, 2015
    Accepted: Sep. 10, 2015
    Published: Nov. 30, 2015
    DOI: 10.11648/j.ajtas.s.2016050201.12
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    Abstract: In this paper, an efficient approach to pattern recognition (classification) is suggested. It is based on minimization of misclassification probability and uses transition from high dimensional problem (dimension p≥2) to one dimensional problem (dimension p=1) in the case of the two classes as well as in the case of several classes with separati... Show More
  • Tolerance Limits on Order Statistics in Future Samples Coming from the Two-Parameter Exponential Distribution

    Nicholas A. Nechval , Konstantin N. Nechval

    Issue: Volume 5, Issue 2-1, March 2016
    Pages: 1-6
    Received: Sep. 09, 2015
    Accepted: Sep. 10, 2015
    Published: Nov. 30, 2015
    DOI: 10.11648/j.ajtas.s.2016050201.11
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    Abstract: This paper presents an innovative approach to constructing lower and upper tolerance limits on order statistics in future samples. Attention is restricted to invariant families of distributions under parametric uncertainty. The approach used here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the s... Show More