Makoto Aoshima
Statistics and Probability · 2009–2016
A researcher in the Science corpus: 3 papers; trusts 20 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 27 citations into it; #11,322 of 12,932 by trust here, #11,487 by citations
- Artificial Intelligence — 12 citations into it; #30,316 of 35,345 by trust here, #34,213 by citations
- Mathematical Physics — 8 citations into it
- Probabilistic & Bayesian Methods — 7 citations into it
Whom Makoto Aoshima trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Muni S. Srivastava
- Songxi Chen
- Z. Bai
- Nicolai Meinshausen
- Iain M. Johnstone
- Arjun K. Gupta
- Yingli Qin
- Jianqing Fan
- James Stephen Marron
- Kazuyoshi Yata
Who trusts Makoto Aoshima overall
Papers from Makoto Aoshima's perspective
Ordered by how strongly the researchers Makoto Aoshima trusts endorse each paper. Same corpus, a different order for every researcher.
- On the distribution of the largest eigenvalue in principal components analysis
- EFFECT OF HIGH DIMENSION: BY AN EXAMPLE OF A TWO SAMPLE PROBLEM
- Eigenvalues of large sample covariance matrices of spiked population models
- Covariance regularization by thresholding
- A two-sample test for high-dimensional data with applications to gene-set testing
- Gene expression correlates of clinical prostate cancer behavior. (Cancer Cell)
- Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution
- A HIGH DIMENSIONAL TWO SAMPLE SIGNIFICANCE TEST
- Basic properties of strong mixing conditions. A survey and some open questions
- p-Values for High-Dimensional Regression
Their papers
- Two-sample tests for high-dimension, strongly spiked eigenvalue models (2016) — 57 citations
- Two-Stage Procedures for High-Dimensional Data (2011) — 101 citations
- PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context (2009) — 67 citations