Ritwik Mitra
Princeton University
10 Papers
8 Citations
Ritwik Mitra is an academic researcher from Princeton University. The author has contributed to research in topics: Estimator & Inference. The author has an hindex of 3, co-authored 10 publications.
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Papers
•Posted Content
Multivariate Analysis of Nonparametric Estimates of Large Correlation Matrices
Ritwik Mitra,Cun-Hui Zhang +1 more
TL;DR: The results prove that when both the number of variables and sample size are large, the spectral error of the nonparametric estimators is of no greater order than that of the latent sample covariance matrix, at least when compared with some of the sharpest known error bounds for the later.
RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines.
TL;DR: An iterative weighting scheme which, when applied to elastic net, a regularized regression method, significantly improves the overall accuracy of predictions, particularly in the highly sensitive response region.
A general framework for frequentist model averaging
TL;DR: In this paper, a general frequentist model averaging framework is proposed to fit a set of candidate models and average over the estimators using data adaptive weights, and the results show the benefits of the proposed approach over traditional model selection approaches.
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•Posted Content
A General Framework For Frequentist Model Averaging
TL;DR: In this article, a general frequentist model averaging framework is proposed to fit a set of candidate models and average over the estimators using certain data adaptive weights, which greatly broadens the scope of existing methodologies under the frequentist approach.
9
•Posted Content
The Benefit of Group Sparsity in Group Inference with De-biased Scaled Group Lasso
Ritwik Mitra,Cun-Hui Zhang +1 more
TL;DR: In this article, confidence regions and approximate chi-squared tests for variable groups in high-dimensional linear regression were studied. But, the existing analyses of low-dimensional projection estimators do not directly carry through for chi-square-based inference of a large group of variables without inflating the sample size by a factor of the group size.
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