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Robert Tibshirani
Researcher at Stanford University
Publications - 620
Citations - 359457
Robert Tibshirani is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Gene expression profiling. The author has an hindex of 147, co-authored 593 publications receiving 326580 citations. Previous affiliations of Robert Tibshirani include University of Toronto & University of California.
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Improved Relapse Prediction in Pediatric Acute Myeloid Leukemia By Deconvolving Lineage-Specific and Cancer-Specific Features in Single-Cell Data
Timothy J. Keyes,Astraea Jager,Michael J. Krueger,Sylvia K. Plevritis,Robert Tibshirani,Richard Aplenc,Garry P. Nolan,Michele S. Redell,Kara L. Davis +8 more
TL;DR: In this paper , the authors presented a computational approach for decomposing high-dimensional single-cell measurements into two components: a lineage specific component that can be used to align cancer cells with specific stages of myeloid development and a cancer-specific component to identify aberrant phenotypes unique to AML cells.
Posted ContentDOI
Penalized regression for left-truncated and right-censored survival data
Sarah F. McGough,Devin Incerti,Svetlana Lyalina,Ryan Copping,Balasubramanian Narasimhan,Robert Tibshirani +5 more
TL;DR: In this paper, a penalized Cox proportional hazards model for left-truncated and right-censored survival data was applied to assess the implications of left truncation adjustment on bias and interpretation.
Posted Content
Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality Metrics
TL;DR: This paper utilizes each metric for hyperparameter optimization in popular dimensionality reduction methods used for visualization and provides quantitative metrics to objectively compare visualizations to their original manifold.
Journal ArticleDOI
Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data
TL;DR: In this paper, an adaptive multi-scale basis for sparse unordered data is proposed, based on treelets, which can be used to represent sparse data in a multiscale manner.
Confidence intervals for the Cox model test error from cross-validation
Min Woo Sun,Robert Tibshirani +1 more
TL;DR: The nested CV idea is generalized to the Cox proportional hazards model and various choices of test error for this setting are explored to achieve superior coverage compared to intervals derived from standard CV.