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- Jelena Bradic, Jianqing Fan, Weiwei Wang
- Journal of the Royal Statistical Society. Series…
- 2011

In high-dimensional model selection problems, penalized least-square approaches have been extensively used. This paper addresses the question of both robustness and efficiency of penalized model selection methods, and proposes a data-driven weighted linear combination of convex loss functions, together with weighted L(1)-penalty. It is completely… (More)

- Jelena Bradic, Jianqing Fan, Jiancheng Jiang
- Annals of statistics
- 2011

High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection. In this paper we establish strong oracle properties of non-concave penalized methods for non-polynomial (NP) dimensional data with censoring in… (More)

- Yinchu Zhu, Jelena Bradic
- ArXiv
- 2017

Models with many signals, high-dimensional models, often impose structures on the signal strengths. The common assumption is that only a few signals are strong and most of the signals are zero or close (collectively) to zero. However, such a requirement might not be valid in many real-life applications. In this article, we are interested in conducting… (More)

- Yinchu Zhu, Jelena Bradic
- 2016

We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model sparsity or the loading vector representing the hypothesis. Providing asymptotically valid methods for testing general linear functions of the regression parameters in… (More)

- Jelena Bradic
- 2015

Understanding efficiency in high dimensional linear models is a longstanding problem of interest. Classical work with smaller dimensional problems dating back to Huber and Bickel has illustrated the benefits of efficient loss functions. When the number of parameters $p$ is of the same order as the sample size $n$, $p \approx n$, an efficiency pattern… (More)

- Alexander Hanbo Li, Jelena Bradic
- ArXiv
- 2015

This paper examines the role and efficiency of the non-convex loss functions for binary classification problems. In particular, we investigate how to design a simple and effective boosting algorithm that is robust to the outliers in the data. The analysis of the role of a particular non-convex loss for prediction accuracy varies depending on the diminishing… (More)

- Ilya O. Ryzhov, Bin Han, Jelena Bradic
- Management Science
- 2016

- Wenjing Yin, Jelena Bradic
- 2015

Classical statistical theory offers validity under restricted assumptions. However, in practice, it is a common approach to perform statistical analysis based on data-driven model selection [1], which guarantees none of results of classical statistical theory. Those results include hypothesis testings and confidence intervals which are useful tools of… (More)

- Jelena Bradic, Gerda Claeskens, Thomas Gueuning
- ArXiv
- 2017

Many scientific and engineering challenges – ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations – require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis test and the corresponding p-value for… (More)

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