Prediction by Supervised Principal Components
From MaRDI portal
Publication:5754925
DOI10.1198/016214505000000628zbMath1118.62326OpenAlexW2019176983WikidataQ60691594 ScholiaQ60691594MaRDI QIDQ5754925
Debashis Paul, Eric Bair, Robert Tibshirani, Trevor Hastie
Publication date: 20 August 2007
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1198/016214505000000628
Factor analysis and principal components; correspondence analysis (62H25) Linear inference, regression (62J99)
Related Items
Canonical thresholding for nonsparse high-dimensional linear regression, An introduction to recent advances in high/infinite dimensional statistics, Supervised singular value decomposition and its asymptotic properties, TPRM: tensor partition regression models with applications in imaging biomarker detection, Forecasting economic time series using targeted predictors, Inference in latent factor regression with clusterable features, The additive hazards model with high-dimensional regressors, Identification of consistent functional genetic modules, Targeted principal components regression, Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks, Identification of relevant subtypes via preweighted sparse clustering, Sparse principal component regression with adaptive loading, Survival prediction and variable selection with simultaneous shrinkage and grouping priors, Grouped feature importance and combined features effect plot, Regularization in statistics, sJIVE: supervised joint and individual variation explained, Using sufficient direction factor model to analyze latent activities associated with breast cancer survival, Multinomial logistic factor regression for multi-source functional block-wise missing data, Projective inference in high-dimensional problems: prediction and feature selection, Ensemble Subset Regression (ENSURE): Efficient High-dimensional Prediction, Convergence and prediction of principal component scores in high-dimensional settings, Principal component regression in GAMLSS applied to Greek–German government bond yield spreads, Are bond returns predictable with real-time macro data?, Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models, Using reference models in variable selection, Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models, Distributed Estimation for Principal Component Analysis: An Enlarged Eigenspace Analysis, Optimal discriminant analysis in high-dimensional latent factor models, Asymptotic properties of bridge estimators in sparse high-dimensional regression models, Mining the factor zoo: estimation of latent factor models with sufficient proxies, Predictive performance of psychological tests: is it better to use items than subscales?, Envelopes and principal component regression, Principal fitted components for dimension reduction in regression, Supervised functional principal component analysis, Penalized orthogonal-components regression for large \(p\) small \(n\) data, Rejoinder: Fisher lecture: Dimension reduction in regression, Bayesian principal component regression with data-driven component selection, Independent screening in high-dimensional exponential family predictors’ space, Prediction of multivariate responses with a selected number of principal components, Incorporating biological information into linear models: a Bayesian approach to the selection of pathways and genes, Nonparametric significance testing and group variable selection, Assessment of evaluation criteria for survival prediction from genomic data, ``Preconditioning for feature selection and regression in high-dimensional problems, Supervised multiway factorization, Unnamed Item, Partial least squares Cox regression for genome-wide data, Testing significance of features by lassoed principal components, Internal validation inferences of significant genomic features in genome-wide screening, Survival prediction using gene expression data: a review and comparison, Sparse wavelet regression with multiple predictive curves, Pruning a sufficient dimension reduction with ap-value guided hard-thresholding, Sufficient forecasting using factor models, The Dantzig Discriminant Analysis with High Dimensional Data, Treelets -- an adaptive multi-scale basis for sparse unordered data, Efficient reconstructions of Common Era climate via integrated nested Laplace approximations, Evaluation of driving risk at different speeds, Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds, High-Dimensional Cox Models: The Choice of Penalty as Part of the Model Building Process, An integrative pathway-based clinical-genomic model for cancer survival prediction, Statistical and Knowledge Supported Visualization of Multivariate Data, Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment, High-dimensional classification using features annealed independence rules, Comment: Fisher lecture: Dimension reduction in regression, Certifiably optimal sparse principal component analysis, Sparse partial least squares regression for on‐line variable selection with multivariate data streams, Cross‐validation and peeling strategies for survival bump hunting using recursive peeling methods, Compressed and Penalized Linear Regression, The Dantzig Selector in Cox's Proportional Hazards Model, Testing for additivity in nonparametric heteroscedastic regression models, Dimension reduction of gene expression data, Nonsparse Learning with Latent Variables, Supervised t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification, Unnamed Item, Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection, Discussion of: Treelets -- an adaptive multi-scale basis for sparse unordered data