IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2008i3p1413-1429.html
   My bibliography  Save this article

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

Author

Listed:
  • Hoshino, Takahiro

Abstract

No abstract is available for this item.

Suggested Citation

  • Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:3:p:1413-1429
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00142-9
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    2. Gerhard Arminger & Bengt Muthén, 1998. "A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm," Psychometrika, Springer;The Psychometric Society, vol. 63(3), pages 271-300, September.
    3. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
    4. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    5. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
    6. Siddhartha Chib & Edward Greenberg & Yuxin Chen, 1998. "MCMC Methods for Fitting and Comparing Multinomial Response Models," Econometrics 9802001, University Library of Munich, Germany, revised 06 May 1998.
    7. Hogan J.W. & Tchernis R., 2004. "Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 314-324, January.
    8. P. Bentler, 1983. "Some contributions to efficient statistics in structural models: Specification and estimation of moment structures," Psychometrika, Springer;The Psychometric Society, vol. 48(4), pages 493-517, December.
    9. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
    10. K. Jöreskog, 1971. "Simultaneous factor analysis in several populations," Psychometrika, Springer;The Psychometric Society, vol. 36(4), pages 409-426, December.
    11. David B. Dunson & Sally D. Perreault, 2001. "Factor Analytic Models of Clustered Multivariate Data with Informative Censoring," Biometrics, The International Biometric Society, vol. 57(1), pages 302-308, March.
    12. Takahiro Hoshino & Hiroshi Kurata & Kazuo Shigemasu, 2006. "A Propensity Score Adjustment for Multiple Group Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 691-712, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fernando A. B. Colugnati & Sergio Firpo & Paula F. Drummond Castro & Juan E. Sepulveda & Sergio L. M. Salles-Filho, 2014. "A propensity score approach in the impact evaluation on scientific production in Brazilian biodiversity research: the BIOTA Program," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 85-107, October.
    2. Edler, Lutz & Lee, Jae Won & Mittlböck, Martina & Niland, Joyce & Victor, Norbert, 2009. "Computational statistics within clinical research," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 583-585, January.
    3. David Kaplan & Jianshen Chen, 2012. "A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 581-609, July.
    4. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    5. Olli Saarela & David A. Stephens & Erica E. M. Moodie & Marina B. Klein, 2015. "On Bayesian estimation of marginal structural models," Biometrics, The International Biometric Society, vol. 71(2), pages 279-288, June.
    6. Kei Miyazaki & Takahiro Hoshino & Shin-ichi Mayekawa & Kazuo Shigemasu, 2009. "A New Concurrent Calibration Method for Nonequivalent Group Design under Nonrandom Assignment," Psychometrika, Springer;The Psychometric Society, vol. 74(1), pages 1-19, March.
    7. Corwin Matthew Zigler, 2016. "The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 47-54, February.
    8. Hoshino, Takahiro, 2008. "Bayesian significance testing and multiple comparisons from MCMC outputs," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3543-3559, March.
    9. Takahiro Hoshino & Ryosuke Igari, 2017. "Quasi-Bayesian Inference for Latent Variable Models with External Information: Application to generalized linear mixed models for biased data," Keio-IES Discussion Paper Series 2017-014, Institute for Economics Studies, Keio University.
    10. Ryosuke Igari & Takahiro Hoshino, 2018. "A Bayesian Gamma Frailty Model Using the Sum of Independent Random Variables: Application of the Estimation of an Interpurchase Timing Model," Keio-IES Discussion Paper Series 2018-021, Institute for Economics Studies, Keio University.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hoshino, Takahiro, 2008. "Bayesian significance testing and multiple comparisons from MCMC outputs," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3543-3559, March.
    2. Takahiro Hoshino & Hiroshi Kurata & Kazuo Shigemasu, 2006. "A Propensity Score Adjustment for Multiple Group Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 691-712, December.
    3. Takahiro Hoshino, 2007. "Doubly Robust-Type Estimation for Covariate Adjustment in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 535-549, December.
    4. Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
    5. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Fast variational Bayes methods for multinomial probit models," Papers 2202.12495, arXiv.org, revised Oct 2022.
    6. Jean-Pierre Florens & Anna Simoni, 2021. "Revisiting Identification Concepts in Bayesian Analysis," Annals of Economics and Statistics, GENES, issue 144, pages 1-38.
    7. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.
    8. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous–discrete covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 803-825, June.
    9. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
    10. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
    11. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
    12. Congdon, Peter, 2009. "Modelling the impact of socioeconomic structure on spatial health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3047-3056, June.
    13. Maura Mezzetti, 2012. "Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 49-74, March.
    14. Cécile Proust & Hélène Jacqmin-Gadda & Jeremy M. G. Taylor & Julien Ganiayre & Daniel Commenges, 2006. "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1014-1024, December.
    15. George Monokroussos, 2013. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 71-105, June.
    16. Takahiro Hoshino & Ryosuke Igari, 2017. "Quasi-Bayesian Inference for Latent Variable Models with External Information: Application to generalized linear mixed models for biased data," Keio-IES Discussion Paper Series 2017-014, Institute for Economics Studies, Keio University.
    17. Hong-Tu Zhu & Sik-Yum Lee, 2001. "A Bayesian analysis of finite mixtures in the LISREL model," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 133-152, March.
    18. David B. Dunson & Zhen Chen & Jean Harry, 2003. "A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 521-530, September.
    19. Hao Sun & Emily Berg & Zhengyuan Zhu, 2022. "Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model," Biometrics, The International Biometric Society, vol. 78(4), pages 1555-1565, December.
    20. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006 390, Society for Computational Economics.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:52:y:2008:i:3:p:1413-1429. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.