1 code implementation • 17 Oct 2022 • Sloan Nietert, Ritwik Sadhu, Ziv Goldfeld, Kengo Kato
The goal of this work is to quantify this scalability from three key aspects: (i) empirical convergence rates; (ii) robustness to data contamination; and (iii) efficient computational methods.
no code implementations • 28 Jul 2021 • Ritwik Sadhu, Ziv Goldfeld, Kengo Kato
This result is then used to derive new empirical convergence rates for classic $W_1$ in terms of the intrinsic dimension.
no code implementations • 12 Feb 2021 • Harold D. Chiang, Kengo Kato, Yuya Sasaki, Takuya Ura
We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints.
no code implementations • 11 Jan 2021 • Sloan Nietert, Ziv Goldfeld, Kengo Kato
Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning.
no code implementations • 10 Sep 2020 • Harold D. Chiang, Kengo Kato, Yuya Sasaki
We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes.
no code implementations • 1 Jun 2020 • Tao Zhang, Kengo Kato, David Ruppert
Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop two bootstrap methods, a novel pivotal bootstrap and the nonparametric bootstrap, for our conditional mode estimator.
Statistics Theory Methodology Statistics Theory