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2 code implementations • 21 Aug 2022 • Xuelin Yang, Louis Abraham, Sejin Kim, Petr Smirnov, Feng Ruan, Benjamin Haibe-Kains, Robert Tibshirani

The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form.

no code implementations • 26 Jan 2022 • Min Woo Sun, Robert Tibshirani

Cross-validation (CV) is one of the most widely used techniques in statistical learning for estimating the test error of a model, but its behavior is not yet fully understood.

no code implementations • 23 Dec 2021 • Daisy Yi Ding, Shuangning Li, Balasubramanian Narasimhan, Robert Tibshirani

Leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.

2 code implementations • 1 Apr 2021 • Stephen Bates, Trevor Hastie, Robert Tibshirani

Cross-validation is a widely-used technique to estimate prediction error, but its behavior is complex and not fully understood.

1 code implementation • 2 Jun 2020 • J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani

In some supervised learning settings, the practitioner might have additional information on the features used for prediction.

no code implementations • 28 Feb 2020 • Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.

Applications

no code implementations • 4 Dec 2019 • J. Kenneth Tay, Robert Tibshirani

Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable.

2 code implementations • 29 Jul 2019 • Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani

Unlike other approaches to feature selection for neural nets, our method uses a modified objective function with constraints, and so integrates feature selection with the parameter learning directly.

1 code implementation • 3 Jul 2019 • Jonathan Johannemann, Robert Tibshirani

Nonlinear dimensionality reduction methods are a popular tool for data scientists and researchers to visualize complex, high dimensional data.

no code implementations • 10 Oct 2018 • J. Kenneth Tay, Jerome Friedman, Robert Tibshirani

We propose a new method for supervised learning, especially suited to wide data where the number of features is much greater than the number of observations.

3 code implementations • 14 Apr 2018 • Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, Nigam Shah

Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set.

1 code implementation • 1 Dec 2017 • Robert Tibshirani, Jerome Friedman

We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of modifying variables.

Methodology

no code implementations • 31 Oct 2017 • Alejandro Schuler, Ken Jung, Robert Tibshirani, Trevor Hastie, Nigam Shah

Using simulations, we show that using synth-validation to select a causal inference method for each study lowers the expected estimation error relative to consistently using any single method.

1 code implementation • 27 Jul 2017 • Trevor Hastie, Robert Tibshirani, Ryan J. Tibshirani

In exciting new work, Bertsimas et al. (2016) showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO) problem.

Methodology Computation

1 code implementation • 1 Jul 2017 • Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H. Shah, Trevor Hastie, Robert Tibshirani

When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials.

1 code implementation • 30 Jun 2017 • Scott Powers, Trevor Hastie, Robert Tibshirani

We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression.

no code implementations • 30 May 2017 • Xiaotong Suo, Victor Minden, Bradley Nelson, Robert Tibshirani, Michael Saunders

Canonical correlation analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables.

no code implementations • 22 Jul 2016 • Stefan Wager, Wenfei Du, Jonathan Taylor, Robert Tibshirani

We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information, and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect.

no code implementations • 8 Dec 2015 • William Fithian, Jonathan Taylor, Robert Tibshirani, Ryan Tibshirani

Extending the selected-model tests of Fithian et al. (2014), we construct p-values for each step in the path which account for the adaptive selection of the model path using the data.

no code implementations • 26 May 2014 • Xiaotong Suo, Robert Tibshirani

We consider regression scenarios where it is natural to impose an order constraint on the coefficients.

no code implementations • 22 Jan 2014 • Samuel M. Gross, Robert Tibshirani

We propose a method for performing sparse supervised canonical correlation analysis (sparse sCCA), a specific case of sparse mCCA when one of the datasets is a vector.

1 code implementation • 16 Jan 2014 • Ryan J. Tibshirani, Jonathan Taylor, Richard Lockhart, Robert Tibshirani

We propose new inference tools for forward stepwise regression, least angle regression, and the lasso.

Methodology 62F03, 62G15

no code implementations • 18 Nov 2013 • Nadine Hussami, Robert Tibshirani

We propose a new sparse regression method called the component lasso, based on a simple idea.

no code implementations • 30 Jan 2013 • Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani

We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an $\operatorname {Exp}(1)$ asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model).

Statistics Theory Methodology Statistics Theory

no code implementations • 22 May 2012 • Jacob Bien, Jonathan Taylor, Robert Tibshirani

We add a set of convex constraints to the lasso to produce sparse interaction models that honor the hierarchy restriction that an interaction only be included in a model if one or both variables are marginally important.

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