IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v59y1994i1p21-47.html
   My bibliography  Save this article

The statistical analysis of general processing tree models with the EM algorithm

Author

Listed:
  • Xiangen Hu
  • William Batchelder

Abstract

No abstract is available for this item.

Suggested Citation

  • Xiangen Hu & William Batchelder, 1994. "The statistical analysis of general processing tree models with the EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 59(1), pages 21-47, March.
  • Handle: RePEc:spr:psycho:v:59:y:1994:i:1:p:21-47
    DOI: 10.1007/BF02294263
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02294263
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/BF02294263?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, vol. 49(3), pages 305-341, September.
    2. Donald Rubin, 1991. "EM and beyond," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 241-254, June.
    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. Jonas Moss, 2023. "Measuring Agreement Using Guessing Models and Knowledge Coefficients," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1002-1025, September.
    2. Steffen Nestler & Edgar Erdfelder, 2023. "Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 809-829, September.
    3. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad, 2021. "A multinomial process tree for reliability assessment of machinery in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. repec:cup:judgdm:v:6:y:2011:i:8:p:814-820 is not listed on IDEAS
    5. Adrian Hoffmann & Julia Meisters & Jochen Musch, 2021. "Nothing but the truth? Effects of faking on the validity of the crosswise model," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-20, October.
    6. Quentin F. Gronau & Eric-Jan Wagenmakers & Daniel W. Heck & Dora Matzke, 2019. "A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 261-284, March.
    7. Marc Jekel & Andreas Glockner & Arndt Broder & Viktoriya Maydych, 2014. "Approximating rationality under incomplete information: Adaptive inferences for missing cue values based on cue-discrimination," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(2), pages 129-147, March.
    8. Florian Wickelmaier & Achim Zeileis, 2016. "Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models," Working Papers 2016-26, Faculty of Economics and Statistics, Universität Innsbruck.
    9. Julia Meisters & Adrian Hoffmann & Jochen Musch, 2020. "Can detailed instructions and comprehension checks increase the validity of crosswise model estimates?," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    10. Morten Moshagen & Benjamin E. Hilbig, 2011. "Methodological notes on model comparisons and strategy classification: A falsificationist proposition," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 814-820, December.
    11. Liu, Yin & Tian, Guo-Liang, 2013. "A variant of the parallel model for sample surveys with sensitive characteristics," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 115-135.
    12. Daniel W. Heck & Edgar Erdfelder & Pascal J. Kieslich, 2018. "Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 893-918, December.
    13. repec:cup:judgdm:v:9:y:2014:i:2:p:129-147 is not listed on IDEAS
    14. Dora Matzke & Conor Dolan & William Batchelder & Eric-Jan Wagenmakers, 2015. "Bayesian Estimation of Multinomial Processing Tree Models with Heterogeneity in Participants and Items," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 205-235, March.
    15. Javier Revuelta, 2008. "The generalized Logit-Linear Item Response Model for Binary-Designed Items," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 385-405, September.

    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. T.R.L. Fry & R.D. Brooks & Br. Comley & J. Zhang, 1993. "Economic Motivations for Limited Dependent and Qualitative Variable Models," The Economic Record, The Economic Society of Australia, vol. 69(2), pages 193-205, June.
    2. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Domanski, Adam, 2009. "Estimating Mixed Logit Recreation Demand Models With Large Choice Sets," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49413, Agricultural and Applied Economics Association.
    4. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-Based Estimation of Latent Generalized ARCH Structures," Econometrica, Econometric Society, vol. 72(5), pages 1481-1517, September.
    5. Andrea B & Benedikt R, 2017. "The Analytical Covariance Matrix for Regime - Switch in Models," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 4(1), pages 1-3, November.
    6. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    7. Mencía, Javier & Sentana, Enrique, 2009. "Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation," Journal of Econometrics, Elsevier, vol. 153(2), pages 105-121, December.
    8. Kenneth J. Wilkins & Garrett M. Fitzmaurice, 2006. "A Hybrid Model for Nonignorable Dropout in Longitudinal Binary Responses," Biometrics, The International Biometric Society, vol. 62(1), pages 168-176, March.
    9. Michael P. Keane & Robert M. Sauer, 2010. "A Computationally Practical Simulation Estimation Algorithm For Dynamic Panel Data Models With Unobserved Endogenous State Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(4), pages 925-958, November.
    10. Hull, Isaiah & Sattath, Or & Diamanti, Eleni & Wendin, Göran, 2020. "Quantum Technology for Economists," Working Paper Series 398, Sveriges Riksbank (Central Bank of Sweden).
    11. Yasutomo Murasawa & Roberto S. Mariano, 2004. "Constructing a Coincident Index of Business Cycles Without Assuming a One-Factor Model," Econometric Society 2004 Far Eastern Meetings 710, Econometric Society.
    12. Keane, Michael, 1993. "Simulation estimation for panel data models with limited dependent variables," MPRA Paper 53029, University Library of Munich, Germany.
    13. Andrea Beccarini, 2016. "Bias correction through filtering omitted variables and instruments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 754-766, March.
    14. Michael P. Keane, 1989. "A computationally practical simulation estimator for panel data, with applications to labor supply and real wage movement over the business cycle," Discussion Paper / Institute for Empirical Macroeconomics 16, Federal Reserve Bank of Minneapolis.
    15. Koutchad, P. & Carpentier, A. & Femenia, F., 2018. "Dealing with corner solutions in multi-crop micro-econometric models: an endogenous regime approach with regime fixed costs," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277530, International Association of Agricultural Economists.
    16. Yuki Takara & Shingo Takagi, 2023. "An empirical approach to measure unobserved cultural relations using music trade data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 47(2), pages 205-245, June.
    17. Koutchade, Philippe & Carpentier, Alain & Féménia, Fabienne, 2015. "Empirical modeling of production decisions of heterogeneous farmers with random parameter models," Working Papers 210097, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    18. David S. Bates, 1997. "Post-'87 Crash Fears in S&P 500 Futures Options," NBER Working Papers 5894, National Bureau of Economic Research, Inc.
    19. Fiorentini, Gabriele & Galesi, Alessandro & Sentana, Enrique, 2018. "A spectral EM algorithm for dynamic factor models," Journal of Econometrics, Elsevier, vol. 205(1), pages 249-279.

    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:spr:psycho:v:59:y:1994:i:1:p:21-47. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.