Yuta Koike
Statistics and Probability · 2019–2022
A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 93 citations into it; #7,619 of 12,932 by trust here, #8,740 by citations
- Artificial Intelligence — 36 citations into it
- Applied Mathematics — 24 citations into it; #5,867 of 7,620 by trust here, #6,655 by citations
- Mathematical Physics — 9 citations into it
- Economics and Econometrics — 7 citations into it
- Geometry and Topology — 7 citations into it; #5,790 of 7,422 by trust here, #6,497 by citations
- Management Science and Operations Research — 7 citations into it
- Discrete Mathematics and Combinatorics — 5 citations into it
Whom Yuta Koike trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sourav Chatterjee
- Victor Chernozhukov
- D. Chetverikov
- Ivan Nourdin
- Gesine Reinert
- Max Fathi
- Alex Zhai
- V. Bentkus
- Kengo Kato
- Adrian Röllin
Who trusts Yuta Koike overall
Papers from Yuta Koike's perspective
Ordered by how strongly the researchers Yuta Koike trusts endorse each paper. Same corpus, a different order for every researcher.
- MULTIVARIATE NORMAL APPROXIMATION USING EXCHANGEABLE PAIRS
- Regression Shrinkage and Selection via the Lasso
- Gaussian approximation of suprema of empirical processes
- The Dantzig selector: Statistical estimation when P is much larger than n
- Normal Approximations with Malliavin Calculus: From Stein's Method to Universality
- Stein’s method on Wiener chaos
- A tail inequality for suprema of unbounded empirical processes with applications to Markov chains
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- CONFIDENCE BANDS IN DENSITY ESTIMATION
- Stein kernels and moment maps (Annals of Probability)
Their papers
- Nearly optimal central limit theorem and bootstrap approximations in high dimensions (2020) — 52 citations
- High-dimensional central limit theorems by Stein’s method (2020) — 51 citations
- Improved central limit theorem and bootstrap approximations in high dimensions (2019) — 95 citations
- High-Dimensional Data Bootstrap (2022) — 53 citations