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Principal component analysis of binary data by iterated singular value decomposition

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  • de Leeuw, Jan

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  • de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:1:p:21-39
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    1. Groenen, P.J.F. & Giaquinto, P. & Kiers, H.A.L., 2003. "Weighted Majorization Algorithms for Weighted Least Squares Decomposition Models," Econometric Institute Research Papers EI 2003-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Dankmar Böhning & Bruce Lindsay, 1988. "Monotonicity of quadratic-approximation algorithms," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 40(4), pages 641-663, December.
    3. Bailey, Michael, 2001. "Ideal Point Estimation with a Small Number of Votes: A Random-Effects Approach," Political Analysis, Cambridge University Press, vol. 9(3), pages 192-210, January.
    4. I. Böckenholt & W. Gaul, 1986. "Analysis of choice behaviour via probabilistic ideal point and vector models," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 2(4), pages 209-226.
    5. Henk Kiers, 1997. "Weighted least squares fitting using ordinary least squares algorithms," Psychometrika, Springer;The Psychometric Society, vol. 62(2), pages 251-266, June.
    6. A. Béguin & C. Glas, 2001. "MCMC estimation and some model-fit analysis of multidimensional IRT models," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 541-561, December.
    7. Yoshio Takane & Jan Leeuw, 1987. "On the relationship between item response theory and factor analysis of discretized variables," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 393-408, September.
    8. Poole, Keith T., 2001. "The Geometry of Multidimensional Quadratic Utility in Models of Parliamentary Roll Call Voting," Political Analysis, Cambridge University Press, vol. 9(3), pages 211-226, January.
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    2. Evans, Gary, 2014. "Analyzing Spatial Models of Choice and Judgment with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(b01).
    3. de Leeuw, Jan & Lange, Kenneth, 2009. "Sharp quadratic majorization in one dimension," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2471-2484, May.
    4. Wang, Fa, 2017. "Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor-augmented regressions," MPRA Paper 93484, University Library of Munich, Germany, revised 19 May 2019.
    5. Groenen, Patrick J. F. & van de Velden, Michel, 2016. "Multidimensional Scaling by Majorization: A Review," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i08).
    6. Jose Giovany Babativa-Márquez & José Luis Vicente-Villardón, 2021. "Logistic Biplot by Conjugate Gradient Algorithms and Iterated SVD," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
    7. Landgraf, Andrew J. & Lee, Yoonkyung, 2020. "Dimensionality reduction for binary data through the projection of natural parameters," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    8. Sayan Chakraborty & Arnab Bhattacharjee & Taps Maiti, 2021. "Structural Factorization of Latent Adjacency Matrix, with an application to Auto Industry Networks," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 185-206, November.
    9. Julio César Hernández-Sánchez & José Luis Vicente-Villardón, 2017. "Logistic biplot for nominal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(2), pages 307-326, June.
    10. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    11. Tangian, Andranik S., 2017. "Selection of questions for VAAs and the VAA-based elections," Working Paper Series in Economics 100, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    12. Tanguiane, Andranick S., 2019. "Combining the third vote with traditional elections," Working Paper Series in Economics 132, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    13. Seokho Lee & Hyejin Shin & Sang Han Lee, 2016. "Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study," Biometrics, The International Biometric Society, vol. 72(4), pages 1325-1335, December.
    14. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
    15. Robin, Geneviève & Josse, Julie & Moulines, Éric & Sardy, Sylvain, 2019. "Low-rank model with covariates for count data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 416-434.
    16. van Dijk, A. & van Rosmalen, J.M. & Paap, R., 2009. "A Bayesian approach to two-mode clustering," Econometric Institute Research Papers EI 2009-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. Fithian, William & Josse, Julie, 2017. "Multiple correspondence analysis and the multilogit bilinear model," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 87-102.
    18. Tasos Kalandrakis, 2006. "Roll Call Data and Ideal Points," Wallis Working Papers WP42, University of Rochester - Wallis Institute of Political Economy.
    19. John B. Holmes & Matthew R. Schofield & Richard J. Barker, 2022. "Pólya‐gamma data augmentation and latent variable models for multivariate binomial data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 194-218, January.
    20. Yang Liu, 2020. "A Riemannian Optimization Algorithm for Joint Maximum Likelihood Estimation of High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 439-468, June.
    21. Wang, Fa, 2022. "Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions," Journal of Econometrics, Elsevier, vol. 229(1), pages 180-200.
    22. Todorov, Diman & Setchi, Rossi, 2014. "Time-efficient estimation of conditional mutual information for variable selection in classification," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 105-127.
    23. Won Chang & Murali Haran & Patrick Applegate & David Pollard, 2016. "Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 57-72, March.

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