Fiducial prediction intervals
By Chih-Ming Wang, Jan Hannig, Hari Iyer. 2012
60 citations recorded by the source corpus.
Abstract This paper presents an approach for constructing prediction intervals for any given distribution. The approach is based on the principle of fiducial inference. We use several examples, including the normal, binomial, exponential, gamma, and Weibull distributions, to illustrate the proposed procedure.
Subfields: Aerospace Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b1f376dea1286c5a8695f6a83a3a122c960137cc
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In Aerospace Engineering
In Statistics and Probability
- Jan Hannig
- Randy C. S. Lai
- Hari Iyer
- Thomas C.M. Lee
- Nicolai Meinshausen
- Peter Buhlmann
- Jelle J. Goeman
- Aldo Solari
Citations in the corpus
- Group bound: confidence intervals for groups of variables in sparse high dimensional regression without assumptions on the design (2013, background): “For a known noise distribution, the noise vector is clearly a pivotal quantity and this is exploited in the argument above and could possibly be extended to fiducial-type inference (Cisewski and Hannig, 2012; Wang et al., 2012; Taraldsen and Lindqvist, 2013).”
- Generalized Fiducial Inference: A Review and New Results (2016, methodology): “…Iyer, and Mathew 2003; Hannig et al. 2006a), problems of metrology (Hannig, Wang, and Iyer 2003; Wang and Iyer 2005, 2006a,b; Hannig, Iyer, and Wang 2007; Wang, Hannig, and Iyer 2012b), and interlaboratory experiments and international key comparison experiments (Iyer,Wang, andMathew2004).”