Estimation of covariance matrices

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Statistics
Statistical inference
Multivariate statistics

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Related publications (30)

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

FANOK: Knockoffs in Linear Time

Quentin Rebjock

We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large-scale feature selection problems. Identifying the knockoff distribution requires solving a large-scale semidefinite progra ...

SIAM PUBLICATIONS2021

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Assyr Abdulle, Giacomo Garegnani, Andrea Zanoni, Grigorios A. Pavliotis

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coeffic ...

2023

Does jackknife scale really matter for accurate large-scale structure covariances?

Ginevra Favole

The jackknife method gives an internal covariance estimate for large-scale structure surveys and allows model-independent errors on cosmological parameters. Using the SDSS-III BOSS CMASS sample, we study how the jackknife size and number of resamplings imp ...

OXFORD UNIV PRESS2021

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: 1000 multi-tracer mock catalogues with redshift evolution and systematics for galaxies and quasars of the final data release

Jean-Paul Richard Kneib, Cheng Zhao, Anand Stéphane Raichoor, Amélie Tamone

We produce 1000 realizations of synthetic clustering catalogues for each type of the tracers used for the baryon acoustic oscillation and redshift space distortion analysis of the Sloan Digital Sky Surveys-IV extended Baryon Oscillation Spectroscopic Surve ...

OXFORD UNIV PRESS2021

Separating Algorithms From Questions and Causal Inference With Unmeasured Exposures: An Application to Birth Cohort Studies of Early Body Mass Index Rebound

Mats Julius Stensrud, Aaron Leor Sarvet

Observational studies reporting on adjusted associations between childhood body mass index (BMI; weight (kg)/height (m)(2)) rebound and subsequent cardiometabolic outcomes have often not paid explicit attention to causal inference, including definition of ...

2021

Testing general relativity on cosmological scales at redshift z similar to 1.5 with quasar and CMB lensing

Cheng Zhao

We test general relativity (GR) at the effective redshift (z) over tilde similar to 1.5 by estimating the statistic E-G, a probe of gravity, on cosmological scales 19 - 190 h(-1)Mpc. This is the highest redshift and largest scale estimation of E-G so far. ...

OXFORD UNIV PRESS2021

Compact atomic descriptors enable accurate predictions via linear models

Kevin Rossi

We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic forces in molecul ...

AIP Publishing2021

CODEX weak lensing mass catalogue and implications on the mass-richness relation

Jean-Paul Richard Kneib, Huanyuan Shan

The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the largest flux limited sample of X-ray clusters at 0.35 < z < 0.65. It was selected from ROSAT data in the 10 000 square degrees of overlap with BOSS, mapping a total number of 2770 hi ...

OXFORD UNIV PRESS2021

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Daniel Kuhn, Viet Anh Nguyen, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...

2022

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