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Prediction by supervised principal components

Bair, Eric; Hastie, Trevor; Paul, Debashis; Tibshirani, … - In: Journal of the American Statistical Association : JASA 101 (2006), pp. 119-137

Persistent link: https://www.econbiz.de/10003309492

Theory and Methods - Prediction by Supervised Principal Components

Bair, Eric; Hastie, Trevor; Paul, Debashis; Tibshirani, … - In: Journal of the American Statistical Association : JASA 101 (2006) 473, pp. 119-137

Persistent link: https://www.econbiz.de/10006604430

Strong rules for discarding predictors in lasso‐type problems

Tibshirani, Robert; Bien, Jacob; Friedman, Jerome; … - In: Journal of the Royal Statistical Society Series B 74 (2012) 2, pp. 245-266

Persistent link: https://www.econbiz.de/10011036375

Estimating the number of clusters in a data set via the gap statistic

Tibshirani, Robert; Walther, Guenther; Hastie, Trevor - In: Journal of the Royal Statistical Society Series B 63 (2001) 2, pp. 411-423

Persistent link: https://www.econbiz.de/10005294602

Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron

Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert - In: International Statistical Review 88 (2020) S1

Persistent link: https://www.econbiz.de/10012409305

Discussion

Witten, Daniela M.; Hastie, Trevor; Tibshirani, Robert - In: Journal of the American Statistical Association 104 (2009) 486, pp. 698-699

Persistent link: https://www.econbiz.de/10004982737

Generalized additive models

Hastie, Trevor; Tibshirani, Robert - 1991 - Repr

Persistent link: https://www.econbiz.de/10004153538

Flexible Discriminant Analysis by Optimal Scoring

Hastie, Trevor; Tibshirani, Robert; Buja, Andreas - In: Journal of the American Statistical Association : JASA 89 (1994) 428, pp. 1255-1270

Persistent link: https://www.econbiz.de/10006636517

Discussion - On Consistency and Sparsity for Principal Components Analysis in High Dimensions

Witten, Daniela M.; Hastie, Trevor; Tibshirani, Robert - In: Journal of the American Statistical Association : JASA 104 (2009) 486, pp. 698-699

Persistent link: https://www.econbiz.de/10008263765

Nonstationary covariance modeling for incomplete data: Monte Carlo EM approach

Matsuo, Tomoko; Nychka, Douglas W.; Paul, Debashis - In: Computational Statistics & Data Analysis 55 (2011) 6, pp. 2059-2073

A multi-resolution basis can provide a useful representation of nonstationary two-dimensional spatial processes that are typically encountered in the geosciences. The main advantages are its flexibility for representing departures from stationarity and importantly the scalability of algorithms...

Persistent link: https://www.econbiz.de/10008864266