Fiducial Generalized Confidence Intervals

By Jan Hannig, Hari Iyer, Paul Patterson. 2006

343 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8c13159dec83304ddf268dd1c7da3db95af6c0c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jan Hannig
2. Wei-Hwa Wu
3. Hsin-neng Hsieh
4. Hari Iyer
5. Yaming Yu
6. E. Lidong
7. Chih-Ming Wang
8. Thomas C.M. Lee

In Management Science and Operations Research

1. Kalimuthu Krishnamoorthy
2. Jan Hannig
3. Hari Iyer
4. Thomas C.M. Lee
5. Wei-Hwa Wu
6. Hsin-neng Hsieh
7. Yin Lin
8. Chih-Ming Wang

In Statistics and Probability

1. Kalimuthu Krishnamoorthy
2. Jan Hannig
3. Yin Lin
4. Randy C. S. Lai
5. Hari Iyer
6. Thomas C.M. Lee
7. Wei-Hwa Wu
8. Hsin-neng Hsieh

Citations in the corpus

• Generalized Fiducial Inference: A Review and New Results (2016, methodology): “Standard sampling techniques such as MCMC, importance sampling, or sequentialMonte Carlo have been successfully implemented, for example, Hannig et al. (2006a), Hannig (2009), Hannig and Lee (2009),Wandler andHannig (2012b), and Cisewski andHannig (2012).”
• Confidence limits and prediction limits for a Weibull distribution based on the generalized variable approach (2009, background): “For more details and other references, we refer to Hannig et al. (2006).”
• To Center or Not to Center: That Is Not the Question—An Ancillarity–Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency (2011, background): “We do not believe this is a coincidence, but rather a reflection of a deep connection between the inference model and the computational model, perhaps along the lines of fiducial inference or structural inference (see Liu and Wu 1999; Hannig, Iyer, and Patterson 2006; Hannig 2009).”
• Generalized confidence interval estimation for the mean of delta-lognormal distribution: an application to New Zealand trawl survey data (2014, positive): “The GCIs based on FGPQs are proven to have asymptotically correct frequentist coverage probability [8].”
• CONFIDENCE LIMITS FOR STRESS STRENGTH RELIABILITY INVOLVING WEIBULL MODELS (2010, background): “Hannig et al. (2006) have noted that the generalized variable procedures are a special case of fiducial inference procedures, and are asymptotically exact in many situations.”
• A new generalized p-value approach for testing equality of coefficients of variation in k normal populations (2011, background): “[20] and Xu and Li [22], the generalized pivotal quantities for μi and σi are given by”
• Fiducial Intervals for Variance Components in an Unbalanced Two-Component Normal Mixed Linear Model (2008, methodology): “Our proposed methods follow the fiducial generalized pivotal quantity (FGPQ)-based interval procedures discussed by Hannig, Iyer, and Patterson (2006) and the generalizations of the fiducial method given in Hannig (2008).”
• Generalized fiducial inference via discretization (2013, background): “Hannig et al. (2006) established a direct connection between fiducial intervals and generalized confidence intervals and proved the asymptotic frequentist correctness of such intervals.”
• Fiducial prediction intervals (2012, methodology): “Recent research results (Hannig et al., 2006; Hannig, 2009a) and many simulation studies show that fiducial inference is a valid statistical method with good operating characteristics.”