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pro vyhledávání: '"Alfons, Andreas"'
Polychoric correlation is often an important building block in the analysis of rating data, particularly for structural equation models. However, the commonly employed maximum likelihood (ML) estimator is highly susceptible to misspecification of the
Externí odkaz:
http://arxiv.org/abs/2407.18835
Although robust statistical estimators are less affected by outlying observations, their computation is usually more challenging. This is particularly the case in high-dimensional sparse settings. The availability of new optimization procedures, main
Externí odkaz:
http://arxiv.org/abs/2311.17563
Autor:
Welz, Max, Alfons, Andreas
Questionnaires in the behavioral and organizational sciences tend to be lengthy: survey measures comprising hundreds of items are the norm rather than the exception. However, literature suggests that the longer a questionnaire takes, the higher the p
Externí odkaz:
http://arxiv.org/abs/2303.07167
For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach using principal component analysis (PCA) has been criticized for focusin
Externí odkaz:
http://arxiv.org/abs/2212.06108
Publikováno v:
Journal of Statistical Software, 103(13), 1-45 (2022)
Mediation analysis is one of the most widely used statistical techniques in the social, behavioral, and medical sciences. Mediation models allow to study how an independent variable affects a dependent variable indirectly through one or more interven
Externí odkaz:
http://arxiv.org/abs/2202.12063
Publikováno v:
In Econometrics and Statistics March 2024
The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem. The algorithm works by adding an approximation of the exact counting norm as a constraint on the model parameters and iteratively
Externí odkaz:
http://arxiv.org/abs/1701.06967
To perform multiple regression, the least squares estimator is commonly used. However, this estimator is not robust to outliers. Therefore, robust methods such as S-estimation have been proposed. These estimators flag any observation with a large res
Externí odkaz:
http://arxiv.org/abs/1506.01223
To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers. Therefore, alternatives as penalized M-estimation or the sparse least trimmed
Externí odkaz:
http://arxiv.org/abs/1501.01208
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