Dyadic analysis for multi-block data in sport surveys analytics
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Publication:6170913
DOI10.1007/s10479-022-04864-4OpenAlexW4284891770MaRDI QIDQ6170913
Rosaria Romano, Domenico Vistocco, Maria Iannario
Publication date: 13 July 2023
Published in: Annals of Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10479-022-04864-4
Cites Work
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- Selecting the number of components in principal component analysis using cross-validation approximations
- Selecting the number of principal components: estimation of the true rank of a noisy matrix
- How many principal components? Stopping rules for determining the number of non-trivial axes revisited
- Generalized Procrustes analysis
- Redundancy analysis: an alternative for canonical correlation analysis
- Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics
- Principal component analysis.
- A rationale and test for the number of factors in factor analysis
- On the use of quantile regression to deal with heterogeneity: the case of multi-block data
- Hypothesis tests for principal component analysis when variables are standardized
- A generalized solution of the orthogonal Procrustes problem
- Linear Programming Techniques for Regression Analysis
- Regression Quantiles
- Quantile Regression
- An Improved Algorithm for Discrete $l_1 $ Linear Approximation
- RELATIONS BETWEEN TWO SETS OF VARIATES
- The elements of statistical learning. Data mining, inference, and prediction
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