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pro vyhledávání: '"Forzani, Liliana"'
Prediction, in regression and classification, is one of the main aims in modern data science. When the number of predictors is large, a common first step is to reduce the dimension of the data. Sufficient dimension reduction (SDR) is a well establish
Externí odkaz:
http://arxiv.org/abs/2306.10537
Most data sets comprise of measurements on continuous and categorical variables. In regression and classification Statistics literature, modeling high-dimensional mixed predictors has received limited attention. In this paper we study the general reg
Externí odkaz:
http://arxiv.org/abs/2110.13091
Publikováno v:
In Computational Statistics and Data Analysis August 2024 196
A constrained multivariate linear model is a multivariate linear model with the columns of its coefficient matrix constrained to lie in a known subspace. This class of models includes those typically used to study growth curves and longitudinal data.
Externí odkaz:
http://arxiv.org/abs/2101.00514
Autor:
Cook, R. Dennis, Forzani, Liliana
Motivated by a recent series of diametrically opposed articles on the relative value of statistical methods for the analysis of path diagrams in the social sciences, we discuss from a primarily theoretical perspective selected fundamental aspects of
Externí odkaz:
http://arxiv.org/abs/2011.06436
Autor:
Dennis Cook, R., Forzani, Liliana
Publikováno v:
In Journal of Business Research November 2023 167
Publikováno v:
In Journal of Multivariate Analysis July 2023 196
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Publikováno v:
In Pattern Recognition December 2022 132
Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical resu
Externí odkaz:
http://arxiv.org/abs/1710.04349