Clusterwise elastic-net regression based on a combined information criterion
DOI10.1007/s11634-021-00489-wOpenAlexW4205421521MaRDI QIDQ6106146
Thomas Verron, Stéphanie Bougeard, Xavier Bry, Ndeye Niang
Publication date: 27 June 2023
Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11634-021-00489-w
ridge regressionmulticollinearityclusterwise regressionelastic-net regularizationtypological regression\textsc{Lasso} regularization
Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Mathematical Theory of Communication
- Semiparametric mixtures of regressions with single-index for model based clustering
- Regularized fuzzy clusterwise ridge regression
- Principal covariates clusterwise regression (PCCR): accounting for multicollinearity and population heterogeneity in hierarchically organized data
- A maximum likelihood methodology for clusterwise linear regression
- Finite mixture regression: a sparse variable selection by model selection for clustering
- Clusterwise PLS regression on a stochastic process
- Clusterwise linear regression
- Prediction with a flexible finite mixture-of-regressions
- Special issue on ``Innovations on model based clustering and classification
- Clusterwise analysis for multiblock component methods
- Fuzzy clusterwise generalized structured component analysis
- A Sparse PLS for Variable Selection when Integrating Omics Data
- Unsupervised Classifier Based on Heuristic Optimization and Maximum Entropy Principle
- PLS Typological Regression: Algorithmic, Classification and Validation Issues
- REBUS‐PLS: A response‐based procedure for detecting unit segments in PLS path modelling
- Prediction for regularized clusterwise multiblock regression
- Supervised Component Generalized Linear Regression with Multiple Explanatory Blocks: THEME-SCGLR
- Regularization and Variable Selection Via the Elastic Net
- Model Selection and Estimation in Regression with Grouped Variables
- Elements of Information Theory
- Mixture of inhomogeneous matrix models for species‐rich ecosystems
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