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• D'Orazio, M., Di Zio, M., Scanu, M., Statistical Matching: Theory and Practice. 2006, John Wiley & Sons, 10.1002/0470023554.
• Anderson, T.W., Maximum likelihood estimates for a multivariate normal distribution when some observations are missing. J. Am. Stat. Assoc. 52:278 (1957), 200–203, 10.1080/01621459.1957.10501379.
• Tonkin, R., Webber, D., Statistical matching of EU-SILC and household budget survey to compare poverty estimates using income, expenditures and material deprivation. EU-SILC International Conference, Vienna, 2012, 6–7, 10.2785/4151.
• Donatiello, G., D'Orazio, M., Frattarola, D., Rizzi, A., Scanu, M., Spaziani, M., Statistical matching of income and consumption expenditures. Int. J. Econ. Sci., 3(3), 2014, 50.
• Serafino, P., Tonkin, R., Statistical Matching of European Union Statistics on Income and Living Conditions (EU-SILC) and the Household Budget Survey. Technical Report, 2017, Eurostat: Statistical Working Papers. Luxembourg: Publications Office of the European Union., 10.2785/933460.
• López-Laborda, J., Marín-González, C., Onrubia-Fernández, J., Estimating Engel curves: a new way to improve the SILC-HBS matching process using GLM methods. J. Appl. Stat. 47 (2020), 1–18, 10.1080/02664763.2020.1796933.
• Saverio, G., Romano, M.C., Gianni, C., Di Zio, M., Marcello, D., Federica, P., Mauro, S., TORELLI, N., Time Use and Labour Force: A Proposal to Integrate the Datathrough Statistical Matching. Technical Report, 2008, Istat-Produzione libraria e centro stampa.
• Rässler, S., Statistical Matching: A Frequentist Theory, Practical Applications, and Alternative Bayesian approaches. 168, 2002, Springer Science & Business Media, 10.1007/978-1-4613-0053-3.
• Kim, J.K., Shao, J., Statistical Methods for Handling Incomplete Data. 2013, Chapman and Hall/CRC, New York, NY, USA, 10.1201/b13981.
• Van Buuren, S., Flexible Imputation of Missing Data. 2018, Chapman and Hall/CRC, New York, 10.1201/9780429492259.
• Fosdick, B.K., DeYoreo, M., Reiter, J.P., et al. Categorical data fusion using auxiliary information. Ann. Appl. Stat. 10:4 (2016), 1907–1929, 10.1214/16-AOAS925.
• Conti, P.L., Marella, D., Scanu, M., How far from identifiability? A systematic overview of the statistical matching problem in a non parametric framework. Commun. Stat. Theory Methods 46:2 (2017), 967–994, 10.1080/03610926.2015.1010005.
• W. Wang, R. Arora, K. Livescu, J. Bilmes, On deep multi-view representation learning: objectives and optimization, arXiv preprint arXiv: 1602.01024(2016).
• Y. Kaloga, P. Borgnat, S.P. Chepuri, P. Abry, A. Habrard, Multiview variational graph autoencoders for canonical correlation analysis, arXiv preprint arXiv: 2010.16132(2020).
• Xiu, X., Miao, Z., Yang, Y., Liu, W., Deep canonical correlation analysis using sparsity-constrained optimization for nonlinear process monitoring. IEEE Trans. Ind. Inf. 18:10 (2022), 6690–6699, 10.1109/TII.2021.3121770.
• Xiu, X., Li, Y., Learning sparse kernel CCA with graph priors for nonlinear process monitoring. IEEE Sens. J. 23:7 (2023), 7381–7389, 10.1109/JSEN.2023.3245832.
• Xiu, X., Pan, L., Yang, Y., Liu, W., Efficient and fast joint sparse constrained canonical correlation analysis for fault detection. IEEE Trans. Neural Netw. Learn. Syst. 35:3 (2024), 4153–4163, 10.1109/TNNLS.2022.3201881.
• Rumelhart, D.E., Hinton, G., Williams, R., Learning internal representations by error propagation. 1986, MIT Press, Cambridge, MA, USA, 318–362.
• L. Luo, J. Xu, J. Lin, Q. Zeng, X. Sun, An auto-encoder matching model for learning utterance-level semantic dependency in dialogue generation, arXiv preprint arXiv: 1808.08795(2018). 10.18653/v1/D18-1075.
• Mitsuhiro, M., Hoshino, T., Kernel canonical correlation analysis for data combination of multiple-source datasets. Jpn J. Stat. Data Sci. 3 (2020), 1–18, 10.1007/s42081-020-00074-z.
• Annoye, H., Beretta, A., Heuchenne, C., Statistical matching using kernel canonical correlation analysis and super-organizing map. Expert Syst. Appl., 2024, 123134, 10.1016/j.eswa.2023.123134.
• D'Orazio, M., Di Zio, M., Scanu, M., Statistical matching for categorical data: displaying uncertainty and using logical constraints. J. Off. Stat Stockholm, 22(1), 2006, 137.
• Lai, P.L., Fyfe, C., Kernel and nonlinear canonical correlation analysis. Int. J. Neural Syst. 10:05 (2000), 365–377, 10.1142/s012906570000034x.
• S. Akaho, A kernel method for canonical correlation analysis, arXiv preprint arXiv: 0609071(2001) 1–7.
• Hotelling, H., Relations between two sets of variates. Biometrika 28 (3-4) (1936), 321–377, 10.2307/2333955.
• Schölkopf, B., Smola, A., Müller, K.-R., Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10:5 (1998), 1299–1319.
• Kuss, M., Graepel, T., The Geometry of Kernel Canonical Correlation Analysis. Technical Report, 2003, Max Planck Institute for Biological Cybernetics.
• Andrew, G., Arora, R., Bilmes, J., Livescu, K., Deep canonical correlation analysis. Dasgupta, S., McAllester, D., (eds.) Proceedings of the 30th International Conference on Machine Learning Proceedings of Machine Learning Research, 28, 2013, PMLR, Atlanta, Georgia, USA, 1247–1255 https://proceedings.mlr.press/v28/andrew13.html.
• Cohen, J., Cohen, P., West, S.G., Aiken, L.R.S., Applied Multiple Regression/Correlation Analysis For The Behavioral Sciences. 2002, Routledge, 10.4324/9780203774441.
• T. Jones, A coefficient of determination for probabilistic topic models, arXiv preprint arXiv: 1911.11061(2019).
• Washizawa, Y., Subset basis approximation of kernel principal component analysis. Principal Component Analysis, 2012, IntechOpen, Croatia, 67–90.
• A. Ben-Israel, T. Greville, Generalized matrix inverses: theory and practice, John Wiley & Sons, New York, NY, USA, 1973.