IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v68y2012i2p567-577.html
   My bibliography  Save this article

Bayesian Lasso for Semiparametric Structural Equation Models

Author

Listed:
  • Ruixin Guo
  • Hongtu Zhu
  • Sy-Miin Chow
  • Joseph G. Ibrahim

Abstract

No abstract is available for this item.

Suggested Citation

  • Ruixin Guo & Hongtu Zhu & Sy-Miin Chow & Joseph G. Ibrahim, 2012. "Bayesian Lasso for Semiparametric Structural Equation Models," Biometrics, The International Biometric Society, vol. 68(2), pages 567-577, June.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:2:p:567-577
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01751.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    2. Sik-Yum Lee & Hong-Tu Zhu, 2002. "Maximum likelihood estimation of nonlinear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 189-210, June.
    3. Ludwig Fahrmeir & Alexander Raach, 2007. "A Bayesian Semiparametric Latent Variable Model for Mixed Responses," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 327-346, September.
    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. Augustin Kelava & Pascal Kilian & Judith Glaesser & Samuel Merk & Holger Brandt, 2022. "Forecasting Intra-individual Changes of Affective States Taking into Account Inter-individual Differences Using Intensive Longitudinal Data from a University Student Dropout Study in Math," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 533-558, June.
    2. Mike K. P. So & Wing Ki Liu & Amanda M. Y. Chu, 2018. "Bayesian Shrinkage Estimation Of Time-Varying Covariance Matrices In Financial Time Series," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 369-404, December.
    3. Pierre-Olivier Bédard, 2015. "The Mobilization of Scientific Evidence by Public Policy Analysts," SAGE Open, , vol. 5(3), pages 21582440156, September.
    4. Xiang-Nan Feng & Hao-Tian Wu & Xin-Yuan Song, 2017. "Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables," Sociological Methods & Research, , vol. 46(4), pages 926-953, November.
    5. Feng, Xiang-Nan & Wang, Yifan & Lu, Bin & Song, Xin-Yuan, 2017. "Bayesian regularized quantile structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 234-248.

    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. Bayerstadler, Andreas & van Dijk, Linda & Winter, Fabian, 2016. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 244-252.
    2. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    3. Anne Musson & Damien Rousselière, 2020. "Exploring the effect of crisis on cooperatives: a Bayesian performance analysis of French craftsmen cooperatives," Applied Economics, Taylor & Francis Journals, vol. 52(25), pages 2657-2678, May.
    4. Prüser, Jan, 2017. "Forecasting US inflation using Markov dimension switching," Ruhr Economic Papers 710, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Armagan, Artin & Dunson, David, 2011. "Sparse variational analysis of linear mixed models for large data sets," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1056-1062, August.
    6. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    7. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    8. Kastner, Gregor, 2019. "Sparse Bayesian time-varying covariance estimation in many dimensions," Journal of Econometrics, Elsevier, vol. 210(1), pages 98-115.
    9. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.
    10. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
    11. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
    12. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    13. Francesca Caselli & Matilde Faralli & Paolo Manasse & Ugo Panizza, 2021. "On the Benefits of Repaying," IMF Working Papers 2021/233, International Monetary Fund.
    14. Mehran Aflakparast & Mathisca de Gunst & Wessel van Wieringen, 2020. "Analysis of Twitter data with the Bayesian fused graphical lasso," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
    15. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    16. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," MPRA Paper 64341, University Library of Munich, Germany.
    17. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    18. Schomaker Michael & Heumann Christian, 2011. "Model Averaging in Factor Analysis: An Analysis of Olympic Decathlon Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-15, January.
    19. Wang, Jiqian & He, Xiaofeng & Ma, Feng & Li, Pan, 2022. "Uncertainty and oil volatility: Evidence from shrinkage method," Resources Policy, Elsevier, vol. 75(C).
    20. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.

    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:bla:biomet:v:68:y:2012:i:2:p:567-577. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.