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Found 6 papers, 0 papers with code
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
We provide a rigorous theoretical guarantee for the confidence interval, demonstrating that the coverage is approximately exact with an explicit convergence rate and allowing for high confidence level inference.
Spectral Ranking Inferences based on General Multiway Comparisons
This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a general and more realistic setup.
Communication-Efficient Distributed Estimation and Inference for Cox's Model
Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model.
Robust High-dimensional Tuning Free Multiple Testing
A stylized feature of high-dimensional data is that many variables have heavy tails, and robust statistical inference is critical for valid large-scale statistical inference.
Ranking Inferences Based on the Top Choice of Multiway Comparisons
The estimated distribution is then used to construct simultaneous confidence intervals for the differences in the preference scores and the ranks of individual items.
Are Latent Factor Regression and Sparse Regression Adequate?
To fill in such an important gap, we also leverage our model as the alternative model to test the sufficiency of the latent factor regression and the sparse linear regression models.
