T
T. Tony Cai
Researcher at University of Pennsylvania
Publications - 592
Citations - 29449
T. Tony Cai is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Estimator & Minimax. The author has an hindex of 80, co-authored 550 publications receiving 24841 citations. Previous affiliations of T. Tony Cai include University of Chicago & University of Oslo.
Papers
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Interval Estimation for a Binomial Proportion
TL;DR: In this paper, the problem of interval estimation of a binomial proportion is revisited, and a number of natural alternatives are presented, each with its motivation and con- text, each interval is examined for its coverage probability and its length.
Journal ArticleDOI
Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
T. Tony Cai,Lie Wang +1 more
TL;DR: It is shown that under conditions on the mutual incoherence and the minimum magnitude of the nonzero components of the signal, the support of the signals can be recovered exactly by the OMP algorithm with high probability.
Journal ArticleDOI
A Constrained ℓ1 Minimization Approach to Sparse Precision Matrix Estimation
T. Tony Cai,Weidong Liu,Xi Luo +2 more
TL;DR: A constrained ℓ1 minimization method for estimating a sparse inverse covariance matrix based on a sample of n iid p-variate random variables and is applied to analyze a breast cancer dataset and is found to perform favorably compared with existing methods.
Guidelines on Urological Infections
Magnus Grabe,Riccardo Bartoletti,T. Tony Cai,Béla Köves,Peter Tenke,Florian M.E. Wagenlehner,Björn Wullt +6 more
TL;DR: It is essential to limit the use of antibiotics in general and fluoroquinolones and cephalosporins in particular, especially in uncomplicated infections and asymptomatic bacteriuria.
Posted Content
A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation
T. Tony Cai,Weidong Liu,Xi Luo +2 more
TL;DR: In this article, a constrained L1 minimization method is proposed for estimating a sparse inverse covariance matrix based on a sample of $n$ iid $p$-variate random variables.