최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기응용통계연구 = The Korean journal of applied statistics, v.34 no.3, 2021년, pp.491 - 505
신주원 (인하대학교 통계학과) , 이경재 (인하대학교 통계학과)
We consider linear regression models in high-dimensional settings (p ≫ n) and compare various classes of priors. The spike and slab prior is one of the most widely used priors for Bayesian regression models, but its model space is vast, resulting in a bad performance in finite samples. As an ...
Barbieri MM, Berger JO (2004). Optimal predictive model selection, The Annals of Statistics, 32, 870-897.
Bhadra A, Datta J, Polson NG, and Willard B (2017). The horseshoe+ estimator of ultra-sparse signals, Bayesian Analysis, 12, 1105-1131.
Carvalho CM, Polson NG, and Scott JG (2010). The horseshoe estimator for sparse signals, Biometrika, 97, 465-480.
Van ES, Oberski DL, and Mulder J (2019). Shrinkage priors for Bayesian penalized regression, Journal of Mathematical Psychology, 89, 31-50.
George EI and McCulloch RE (1993). Variable selection via gibbs sampling, Journal of the American Statistical Association, 88, 881-889.
Hoerl AE and Kennard RW (1970). Ridge regression: applications to nonorthogonal problem, Technometrics, 12, 69-82.
Ishwaran H and Rao JS (2005). Spike and slab variable selection: frequentist and Bayesian strategies, The Annals of Statistics, 33, 730-773.
Lee SY, Pati D, and Mallick BK (2020). Continuous Shrinkage Prior Revisited: A Collapsing Behavior and Remedy, arXiv preprint arXiv:2007.02192.
Makalic E and Schmidt DF (2015). A simple sampler for the horseshoe estimator, IEEE Signal Processing Letters, 23, 179-182.
Makalic E and Schmidt DF (2016). High-Dimensional Bayesian Regularised Regression with the Bayesreg Package, arXiv preprint arXiv:1611.06649.
Martin R, Mess R, and Walker SG (2017). Empirical bayes posterior concentration in sparse high-dimensional linear models, Bernoulli, 23, 1822-1847.
Piironen J and Vehtari A (2017). On the hyperprior choice for the global shrinkage parameter in the horseshoe prior. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 905-913.
Piironen J and Vehtari A (2017) . Sparsity information and regularization in the horseshoe and other shrinkage priors, Electronic Journal of Statistics, 11, 5018-5051.
Tibshirani R (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological), 58, 267-288.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.