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Robert Tibshirani

Bio: Robert Tibshirani is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Elastic net regularization. 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.


Papers
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Journal ArticleDOI
TL;DR: The capability of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is established to distinguish between micrometer-sized tumor aggregates of basal cell carcinoma (BCC), a common skin cancer, and normal human skin.
Abstract: Detection of microscopic skin lesions presents a considerable challenge in diagnosing early-stage malignancies as well as in residual tumor interrogation after surgical intervention. In this study, we established the capability of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to distinguish between micrometer-sized tumor aggregates of basal cell carcinoma (BCC), a common skin cancer, and normal human skin. We analyzed 86 human specimens collected during Mohs micrographic surgery for BCC to cross-examine spatial distributions of numerous lipids and metabolites in BCC aggregates versus adjacent skin. Statistical analysis using the least absolute shrinkage and selection operation (Lasso) was employed to categorize each 200-µm-diameter picture element (pixel) of investigated skin tissue map as BCC or normal. Lasso identified 24 molecular ion signals, which are significant for pixel classification. These ion signals included lipids observed at m / z 200–1,200 and Krebs cycle metabolites observed at m / z

61 citations

Posted Content
TL;DR: In this paper, an exact distribution-based method for hypothesis testing and construction of confidence intervals for signals in a noisy matrix is proposed, which is based on the approach of Taylor, Loftus and Tibshirani for testing the global null.
Abstract: Principal component analysis (PCA) is a well-known tool in multivariate statistics. One significant challenge in using PCA is the choice of the number of components. In order to address this challenge, we propose an exact distribution-based method for hypothesis testing and construction of confidence intervals for signals in a noisy matrix. Assuming Gaussian noise, we use the conditional distribution of the singular values of a Wishart matrix and derive exact hypothesis tests and confidence intervals for the true signals. Our paper is based on the approach of Taylor, Loftus and Tibshirani (2013) for testing the global null: we generalize it to test for any number of principal components, and derive an integrated version with greater power. In simulation studies we find that our proposed methods compare well to existing approaches.

61 citations

Journal ArticleDOI
TL;DR: There are tantalizing similarities between the Dantzig Selector (DS) and the LARS methods, but they are not the same and produce somewhat different models.
Abstract: Discussion of ``The Dantzig selector: Statistical estimation when $p$ is much larger than $n$'' [math/0506081]

60 citations

Journal ArticleDOI
15 Aug 1997-Cancer
TL;DR: The efficacy of breast carcinoma screening should be enhanced if false‐negative mammography were reduced, and menstrual cycle phase was associated with false‐ negative outcomes for mammographic screening.
Abstract: BACKGROUND The efficacy of breast carcinoma screening should be enhanced if false-negative mammography were reduced. Prospectively collected data from the Canadian National Breast Screening Study were used to examine whether menstrual cycle phase was associated with false-negative outcomes for mammographic screening. METHODS Of 8887 women ages 40-44 years at the onset of screening, randomized to receive annual mammography and clinical breast examination, reporting menstruation no more than 28 days prior to their screening examination, and with a valid radiologic report, 1898 had never used oral contraceptives or replacement estrogen with or without progesterone. The remainder were past (6573) and current (416) estrogen users. Similar selection criteria were applied at subsequent screens. The distribution of false-negative and false-positive mammography in relation to true-negative and true-positive mammography was examined with respect to the follicular (Days 1 to 14) and luteal (Days 15-28) menstrual phases. RESULTS Comparing luteal with follicular mammograms in 6989 patients who ever used estrogen, the unadjusted odds ratio (2-sided P -values) for false-negatives versus true-negatives was 2.16 (0.05) and the adjusted odds ratio was 1.47 (0.05). In 1898 never-users, parallel odds ratios for luteal false-negatives were 0.55 (1.0) and 0.74 (1.0), respectively. CONCLUSIONS These results suggest that menstruating women who have used hormones may have an increased risk of false-negative results for screening mammograms performed in the luteal phase of the menstrual cycle. An increased risk of false-negative mammography might adversely affect screening efficacy. The impact of menstrual phase on mammographic interpretation, especially for women who ever used hormones, requires further investigation. Cancer 1997; 80:720-4. © 1997 American Cancer Society.

59 citations


Cited by
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Journal Article
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

47,974 citations

Journal ArticleDOI
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

47,038 citations

Journal ArticleDOI
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Abstract: SUMMARY We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.

40,785 citations

Proceedings ArticleDOI
07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

40,257 citations

Book
18 Nov 2016
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

38,208 citations