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Robert Tibshirani
Proquest Dissertations
A Notation and Definitions
Ryan Tibshirani
High-Dimensional and Causal Inference by Simon James
Pcredux Package - an Overview Stefan Rödiger, Michał Burdukiewicz, Andrej-Nikolai Spiess 2017-11-20
Mathematics People
Arxiv:1905.02086V1 [Stat.CO] 6 May 2019 K Arise When Looking for the Optimal Regularization Parameter in Q X Sˆg(Q) = Rt(Qi + L)−1R
Lester Mackey
T. Hastie, R. Tibshirani, J. Friedman the Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition
The Lasso: an Application to Cancer Detection and Some New Tools for Selective Inference
Downloaded from the Gene Expression Omnibus (GEO) Database Under the Accession Number GSE25219
Additive Logistic Regression: a Statistical View of Boosting
Muddling Labels for Regularization, a Novel Approach to Generalization
Pnas11052ackreviewers 5098..5136
Statistical Learning and Sparsity with Applications to Biomedicine Rob Tibshirani Departments of Biomedical Data Science & Statistics Stanford University
Large-Scale Statistical Learning Methods and Algorithms
© Copyright 2017 Nathanial E. Watson
Trustees' Report and Financial Statements 2019-2020
Top View
Acknowledgment of Reviewers, 2014
Michel Foucault Ronald C Kessler Graham Colditz Sigmund Freud
Institut Für Informatik XII Diplomarbeit Structure Alignment Based
Dreamai: Algorithm for the Imputation of Proteomics Data
AC-PCA Adjusts for Confounding Variation in Transcriptome Data and Recovers the Anatomical Structure of Neocortex
ROBERT JOHN TIBSHIRANI Professor of Statistics and of Biomedical Data Science
Machine Learning Approaches for Identifying Microrna Targets and Conserved Protein Complexes
The Importance of Transparency and Reproducibility in Artificial Intelligence Research
Stable Sparse K–Means Method
Computational Methods to Estimate Error Rates for Peptide Identifications in Mass Spectrometry-Based Proteomics
Additive Logistic Regression: a Statistical View of Boosting
Extrinsic Regularization in Parameter Optimization for Support Vector Machines
Evidence Contrary to the Statistical View of Boosting
The Elements of Statistical Learning Data Mining, Inference, and Prediction
Statistical Models for Analyzing Human Genetic Variation
Multilingual Distributional Lexical Similarity