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Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

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  • Wai-Yin Poon
  • Hai-Bin Wang

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  • Wai-Yin Poon & Hai-Bin Wang, 2010. "Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 498-520, September.
    • Handle: RePEc:spr:psycho:v:75:y:2010:i:3:p:498-520
    DOI: 10.1007/s11336-010-9164-6
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    References listed on IDEAS

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    1. Qihua Wang & J. N. K. Rao, 2002. "Empirical Likelihood‐based Inference in Linear Models with Missing Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 563-576, September.
    2. Wang, Qihua & Yu, Keming, 2007. "Likelihood-based kernel estimation in semiparametric errors-in-covariables models with validation data," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 455-480, March.
    3. Qihua Wang, 2002. "Empirical likelihood-based inference in linear errors-in-covariables models with validation data," Biometrika, Biometrika Trust, vol. 89(2), pages 345-358, June.
    4. Kevin Y Au, 1999. "Intra-cultural Variation: Evidence and Implications for International Business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 30(4), pages 799-812, December.
    5. Haibo Zhou & Jianwei Chen & Jianwen Cai, 2002. "Random Effects Logistic Regression Analysis with Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 58(2), pages 352-360, June.
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    Cited by:

    1. Rana, Subrata & Roy, Surupa & Das, Kalyan, 2018. "Analysis of ordinal longitudinal data under nonignorable missingness and misreporting: An application to Alzheimer’s disease study," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 62-77.
    2. Qiu, Shi-Fang & Poon, Wai-Yin & Tang, Man-Lai, 2016. "Confidence intervals for an ordinal effect size measure based on partially validated series," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 170-192.
    3. Lizbeth Naranjo & Luz Judith R. Esparza & Carlos J. Pérez, 2020. "A Hidden Markov Model to Address Measurement Errors in Ordinal Response Scale and Non-Decreasing Process," Mathematics, MDPI, vol. 8(4), pages 1-12, April.
    4. Tong-Yu Lu & Wai-Yin Poon & Siu Cheung, 2014. "A Unified Framework for the Comparison of Treatments with Ordinal Responses," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 605-620, October.
    5. Tang, Man-Lai & Qiu, Shi-Fang & Poon, Wai-Yin, 2012. "Confidence interval construction for disease prevalence based on partial validation series," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1200-1220.
    6. Debjit Sengupta & Tathagata Banerjee & Surupa Roy, 2020. "Estimation of Poisson mean with under‐reported counts: a double sampling approach," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 508-535, December.
    7. Liu, Feng & Zhao, Shaoqiong & Li, Yang, 2017. "How many, how often, and how new? A multivariate profiling of mobile app users," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 71-80.
    8. Surupa Roy & Kalyan Das & Angshuman Sarkar, 2013. "Analysis of binary data with the possibility of wrong ascertainment," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 293-310, August.

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