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Generalized fiducial inference for wavelet regression

Citations

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Cited by:

  1. Hsin-I Lee & Hungyen Chen & Hirohisa Kishino & Chen-Tuo Liao, 2016. "A Reference Population-Based Conformance Proportion," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 684-697, December.
  2. Nicholas Tibor Longford, 2016. "Decision Theory Applied to Selecting the Winners, Ranking, and Classification," Journal of Educational and Behavioral Statistics, , vol. 41(4), pages 420-442, August.
  3. Yixuan Zou & Jan Hannig & Derek S. Young, 2021. "Generalized fiducial inference on the mean of zero-inflated Poisson and Poisson hurdle models," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-15, December.
  4. Shin-Fu Tsai, 2019. "Comparing Coefficients Across Subpopulations in Gaussian Mixture Regression Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 610-633, December.
  5. Weizhong Tian & Yaoting Yang & Tingting Tong, 2022. "Confidence Intervals Based on the Difference of Medians for Independent Log-Normal Distributions," Mathematics, MDPI, vol. 10(16), pages 1-14, August.
  6. Yang Liu & Ji Seung Yang, 2018. "Interval Estimation of Latent Variable Scores in Item Response Theory," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 259-285, June.
  7. Theerapong Kaewprasert & Sa-Aat Niwitpong & Suparat Niwitpong, 2022. "Simultaneous Confidence Intervals for the Ratios of the Means of Zero-Inflated Gamma Distributions and Its Application," Mathematics, MDPI, vol. 10(24), pages 1-22, December.
  8. Friedrich, Thomas & Knapp, Guido, 2013. "Generalised interval estimation in the random effects meta regression model," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 165-179.
  9. Seungyong Hwang & Randy C. S. Lai & Thomas C. M. Lee, 2022. "Generalized Fiducial Inference for Threshold Estimation in Dose–Response and Regression Settings," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 109-124, March.
  10. Piero Veronese & Eugenio Melilli, 2015. "Fiducial and Confidence Distributions for Real Exponential Families," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 471-484, June.
  11. Beare, Stephen & Chambers, Raymond, 2012. "Fiducial cost-benefit analysis research: with an application to weather modification," 2012 Conference (56th), February 7-10, 2012, Fremantle, Australia 124232, Australian Agricultural and Resource Economics Society.
  12. Wu, Suofei & Hannig, Jan & Lee, Thomas C.M., 2022. "Uncertainty quantification for honest regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
  13. Shin‐Fu Tsai, 2020. "Approximate two‐sided tolerance intervals for normal mixture distributions," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 367-382, September.
  14. David R. Bickel, 2014. "Small-scale Inference: Empirical Bayes and Confidence Methods for as Few as a Single Comparison," International Statistical Review, International Statistical Institute, vol. 82(3), pages 457-476, December.
  15. Yang Liu & Jan Hannig, 2017. "Generalized Fiducial Inference for Logistic Graded Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1097-1125, December.
  16. Piero Veronese & Eugenio Melilli, 2021. "Confidence Distribution for the Ability Parameter of the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 131-166, March.
  17. Hannig, Jan & Lai, Randy C.S. & Lee, Thomas C.M., 2014. "Computational issues of generalized fiducial inference," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 849-858.
  18. Nicholas T. Longford, 2020. "Performance assessment as an application of causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1363-1385, October.
  19. Li, Xinmin & Wang, Juan & Liang, Hua, 2011. "Comparison of several means: A fiducial based approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1993-2002, May.
  20. Xiong, Shifeng, 2011. "An asymptotics look at the generalized inference," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 336-348, February.
  21. Russell J. Bowater, 2017. "A defence of subjective fiducial inference," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(2), pages 177-197, April.
  22. Li, Xinmin & Zhou, Xiaohua & Tian, Lili, 2013. "Interval estimation for the mean of lognormal data with excess zeros," Statistics & Probability Letters, Elsevier, vol. 83(11), pages 2447-2453.
  23. Yang Liu & Jan Hannig, 2016. "Generalized Fiducial Inference for Binary Logistic Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 81(2), pages 290-324, June.
  24. Veronese, Piero & Melilli, Eugenio, 2018. "Some asymptotic results for fiducial and confidence distributions," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 98-105.
  25. Nancy Reid & David R. Cox, 2015. "On Some Principles of Statistical Inference," International Statistical Review, International Statistical Institute, vol. 83(2), pages 293-308, August.
  26. Ionut Bebu & George Luta & Thomas Mathew & Brian K. Agan, 2016. "Generalized Confidence Intervals and Fiducial Intervals for Some Epidemiological Measures," IJERPH, MDPI, vol. 13(6), pages 1-13, June.
  27. Michael P. Fay & Michael A. Proschan & Erica Brittain, 2015. "Combining one-sample confidence procedures for inference in the two-sample case," Biometrics, The International Biometric Society, vol. 71(1), pages 146-156, March.
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