Jianfeng Yao

Statistics and Probability · with Bai · 2004–2018

A researcher in the Science corpus: 14 papers; trusts 58 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 123 citations into it; #1,047 of 12,932 by trust here, #1,262 by citations
• Mathematical Physics — 48 citations into it; #799 of 8,562 by trust here, #810 by citations
• Discrete Mathematics and Combinatorics — 40 citations into it; #1,304 of 2,017 by trust here, #1,251 by citations
• Artificial Intelligence — 34 citations into it; #21,406 of 35,345 by trust here, #25,040 by citations
• Economics and Econometrics — 24 citations into it
• Probabilistic & Bayesian Methods — 24 citations into it
• Computational Theory and Mathematics — 16 citations into it; #5,365 of 12,182 by trust here, #5,465 by citations
• Finance — 15 citations into it; #10,232 of 10,518 by trust here, #7,920 by citations

Whom Jianfeng Yao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Z. Bai
2. Iain M. Johnstone
3. J. Silverstein
4. Muni S. Srivastava
5. Leonid A. Pastur
6. Arthur P. Dempster
7. Olivier Ledoit
8. Florent Benaych-Georges
9. Jinho Baik
10. Jushan Bai

Who trusts Jianfeng Yao overall

1. Zeng Li
2. Debashis Paul
3. Tommaso Cai
4. Z. Bai
5. Z. Bao
6. Qinwen Wang
7. Antti Knowles
8. Edgar Dobriban
9. Alexander Aue
10. Sourav Chatterjee

Papers from Jianfeng Yao's perspective

Ordered by how strongly the researchers Jianfeng Yao trusts endorse each paper. Same corpus, a different order for every researcher.

1. On the distribution of the largest eigenvalue in principal components analysis
2. Eigenvalues of large sample covariance matrices of spiked population models
3. CLT for linear spectral statistics of large-dimensional sample covariance matrices
4. Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
5. Covariance regularization by thresholding
6. Regularized estimation of large covariance matrices
7. Random Matrices
8. Determining the Number of Factors in Approximate Factor Models
9. Spectrum estimation for large dimensional covariance matrices using random matrix theory
10. An Introduction to Multivariate Statistical Analysis

Their papers

• Central limit theorems for eigenvalues in a spiked population model (2008) — 238 citations
• Corrections to LRT on large-dimensional covariance matrix by RMT (2009) — 266 citations
• On the convergence of the spectral empirical process of Wigner matrices (2005) — 123 citations
• Identifying the number of factors from singular values of a large sample auto-covariance matrix (2014) — 47 citations
• On the sphericity test with large-dimensional observations (2013) — 76 citations
• On sample eigenvalues in a generalized spiked population model (2008) — 181 citations
• Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing (2014) — 97 citations
• ON ESTIMATION OF THE POPULATION SPECTRAL DISTRIBUTION FROM A HIGH‐DIMENSIONAL SAMPLE COVARIANCE MATRIX (2010) — 59 citations
• Large Sample Covariance Matrices and High-Dimensional Data Analysis (2015) — 182 citations
• A note on a Marčenko–Pastur type theorem for time series (2011) — 62 citations
• On testing for high-dimensional white noise (2018) — 48 citations
• On estimation of the noise variance in high dimensional probabilistic principal component analysis (2013) — 55 citations
• Self-Excited Threshold Poisson Autoregression (2013) — 109 citations
• Tail of a linear diffusion with Markov switching (2004) — 59 citations