The following publications are possibly variants of this publication:
- Constructing unbiased prediction limits on future outcomes under parametric uncertainty of underlying models via pivotal quantity averaging approachNicholas A. Nechval, Gundars Berzins, S. Balina, I. Steinbuka, Konstantin N. Nechval. accs, 51(5):331-336, 2017. [doi]
- Tolerance limits on order statistics in future samples coming from the Pareto distributionNicholas A. Nechval, Konstantin N. Nechval, S. P. Prisyazhnyuk, V. F. Strelchonok. accs, 50(6):423-431, 2016. [doi]
- A Unified Technique for Prediction and Optimization of Future Outcomes under Parametric Uncertainty via Pivotal Quantities and Ancillary StatisticsNicholas A. Nechval, Gundars Berzins, Konstantin N. Nechval. accs, 57(3):234-257, June 2023. [doi]
- A New Technique of Invariant Statistical Embedding and Averaging Via Pivotal Quantities for Intelligent Constructing Efficient Statistical Decisions under Parametric UncertaintyNicholas A. Nechval, Gundars Berzins, Konstantin N. Nechval. accs, 54(3):191-206, 2020. [doi]
- Finding Prediction Limits for a Future Number of Failures in the Prescribed Time Interval under Parametric UncertaintyNicholas A. Nechval, Maris Purgailis, Uldis Rozevskis, Inta Bruna, Konstantin N. Nechval. asmta 2012: 286-301 [doi]
- Unbiased Simultaneous Prediction Limits on Observations in Future SamplesNicholas A. Nechval, Konstantin N. Nechval, Maris Purgailis, Uldis Rozevskis. asmta 2013: 292-307 [doi]