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pro vyhledávání: '"Johnstone, Iaín M."'
It is now well documented that genetic covariance between functionally related traits leads to an uneven distribution of genetic variation across multivariate trait combinations, and possibly a large part of phenotype-space that is inaccessible to ev
Externí odkaz:
http://arxiv.org/abs/2210.11709
This paper studies spin glass to paramagnetic transition in the Spherical Sherrington-Kirkpatrick model with ferromagnetic Curie-Weiss interaction with coupling constant $J$ and inverse temperature $\beta$. The disorder of the system is represented b
Externí odkaz:
http://arxiv.org/abs/2104.07629
We derive a Central Limit Theorem (CLT) for $\log \left\vert\det \left( W_{N}-E_{N}\right)\right\vert,$ where $W_{N}$ is a Wigner matrix, and $E_{N}$ is local to the edge of the semi-circle law. Precisely, $E_N=2+N^{-2/3}\sigma_N$ with $\sigma_N$ bei
Externí odkaz:
http://arxiv.org/abs/2011.13723
Akademický článek
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Autor:
Johnstone, Iain M., Yang, Jeha
These expository notes serve as a reference for an accompanying post Morales-Jimenez et al. [2018]. In the spiked covariance model, we develop results on asymptotic normality of sample leading eigenvalues and certain projections of the corresponding
Externí odkaz:
http://arxiv.org/abs/1810.10427
Sample correlation matrices are employed ubiquitously in statistics. However, quite surprisingly, little is known about their asymptotic spectral properties for high-dimensional data, particularly beyond the case of "null models" for which the data i
Externí odkaz:
http://arxiv.org/abs/1810.10214
We study principal components analyses in multivariate random and mixed effects linear models, assuming a spherical-plus-spikes structure for the covariance matrix of each random effect. We characterize the behavior of outlier sample eigenvalues and
Externí odkaz:
http://arxiv.org/abs/1806.09529
Autor:
Yang, Jeha, Johnstone, Iain M.
We study improved approximations to the distribution of the largest eigenvalue $\hat{\ell}$ of the sample covariance matrix of $n$ zero-mean Gaussian observations in dimension $p+1$. We assume that one population principal component has variance $\el
Externí odkaz:
http://arxiv.org/abs/1710.06899
Autor:
Mukherjee, Gourab, Johnstone, Iain M.
We study predictive density estimation under Kullback-Leibler loss in $\ell_0$-sparse Gaussian sequence models. We propose proper Bayes predictive density estimates and establish asymptotic minimaxity in sparse models. A surprise is the existence of
Externí odkaz:
http://arxiv.org/abs/1707.04380
Autor:
Fan, Zhou, Johnstone, Iain M.
We study the sample covariance matrix for real-valued data with general population covariance, as well as MANOVA-type covariance estimators in variance components models under null hypotheses of global sphericity. In the limit as matrix dimensions in
Externí odkaz:
http://arxiv.org/abs/1707.02352