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Stochastic Approximation in Monte Carlo Computation

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  • Liang, Faming
  • Liu, Chuanhai
  • Carroll, Raymond J.

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  • Liang, Faming & Liu, Chuanhai & Carroll, Raymond J., 2007. "Stochastic Approximation in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 305-320, March.
    • Handle: RePEc:bes:jnlasa:v:102:y:2007:p:305-320
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    Citations

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    Cited by:

    1. Yukito Iba & Nen Saito & Akimasa Kitajima, 2014. "Multicanonical MCMC for sampling rare events: an illustrative review," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 611-645, June.
    2. Jin, Ick Hoon & Liang, Faming, 2014. "Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 402-416.
    3. Cheon, Sooyoung & Kim, Jaehee, 2010. "Multiple change-point detection of multivariate mean vectors with the Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 406-415, February.
    4. Jaehee Kim & Sooyoung Cheon, 2010. "A Bayesian regime‐switching time‐series model," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 365-378, September.
    5. Shiqiang Jin & Gyuhyeong Goh, 2021. "Bayesian selection of best subsets via hybrid search," Computational Statistics, Springer, vol. 36(3), pages 1991-2007, September.
    6. Chih-Sheng Hsieh & Michael D. König & Xiaodong Liu, 2012. "Network formation with local complements and global substitutes: the case of R&D networks," ECON - Working Papers 217, Department of Economics - University of Zurich, revised Feb 2017.
    7. Anindya Bhadra & Bani K. Mallick, 2013. "Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis," Biometrics, The International Biometric Society, vol. 69(2), pages 447-457, June.
    8. Faming Liang & Ick Hoon Jin & Qifan Song & Jun S. Liu, 2016. "An Adaptive Exchange Algorithm for Sampling From Distributions With Intractable Normalizing Constants," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 377-393, March.
    9. Yangqing Deng & Yinqiu He & Gongjun Xu & Wei Pan, 2022. "Speeding up Monte Carlo simulations for the adaptive sum of powered score test with importance sampling," Biometrics, The International Biometric Society, vol. 78(1), pages 261-273, March.
    10. Brian D. Segal & Thomas Braun & Michael R. Elliott & Hui Jiang, 2018. "Fast approximation of small p†values in permutation tests by partitioning the permutations," Biometrics, The International Biometric Society, vol. 74(1), pages 196-206, March.
    11. Wang, Liqun & Lee, Chel Hee, 2014. "Discretization-based direct random sample generation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1001-1010.
    12. Faming Liang & Momiao Xiong, 2013. "Bayesian Detection of Causal Rare Variants under Posterior Consistency," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    13. Adrian E. Raftery & Le Bao, 2010. "Estimating and Projecting Trends in HIV/AIDS Generalized Epidemics Using Incremental Mixture Importance Sampling," Biometrics, The International Biometric Society, vol. 66(4), pages 1162-1173, December.
    14. Zhang, Shibin, 2016. "Adaptive spectral estimation for nonstationary multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 330-349.
    15. Liu Baisen & Wang Liangliang & Cao Jiguo, 2018. "Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes," Monte Carlo Methods and Applications, De Gruyter, vol. 24(2), pages 117-127, June.
    16. Jaehee Kim & Sooyoung Cheon, 2010. "Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo," Computational Statistics, Springer, vol. 25(2), pages 215-239, June.
    17. Qifan Song & Faming Liang, 2015. "High-Dimensional Variable Selection With Reciprocal L 1 -Regularization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1607-1620, December.
    18. Liang, Faming, 2009. "On the use of stochastic approximation Monte Carlo for Monte Carlo integration," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 581-587, March.
    19. Liang, Faming & Zhang, Jian, 2009. "Learning Bayesian networks for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 865-876, February.

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