English
Related papers

Related papers: On estimating cosmology-dependent covariance matri…

200 papers

An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo-$C_\ell$ estimates of tomographic cosmic shear power spectra. Using two…

We propose a simple method to estimate the parameters of a continuously measured quantum system, by fitting correlation functions of the measured signal. We demonstrate the approach in simulation, both on toy examples and on a recent…

Quantum Physics · Physics 2024-10-17 Pierre Guilmin , Pierre Rouchon , Antoine Tilloy

We present methods for emulating the matter power spectrum by combining information from cosmological $N$-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter…

Cosmology and Nongalactic Astrophysics · Physics 2021-11-17 Ming-Feng Ho , Simeon Bird , Christian R. Shelton

The estimation of cosmological constraints from observations of the large scale structure of the Universe, such as the power spectrum or the correlation function, requires the knowledge of the inverse of the associated covariance matrix,…

Cosmology and Nongalactic Astrophysics · Physics 2015-11-04 Dante J. Paz , Ariel G. Sanchez

Estimating large covariance and precision matrices are fundamental in modern multivariate analysis. The problems arise from statistical analysis of large panel economics and finance data. The covariance matrix reveals marginal correlations…

Methodology · Statistics 2015-04-17 Jianqing Fan , Yuan Liao , Han Liu

The accurate computation of the covariance matrix of fitted model parameters is a somewhat neglected task in Statistics. Algorithms are given for computing accurate covariance matrices derived from computing the Hessian matrix by numerical…

Computation · Statistics 2021-05-12 Rose Baker

Covariance matrix estimation concerns the problem of estimating the covariance matrix from a collection of samples, which is of extreme importance in many applications. Classical results have shown that $O(n)$ samples are sufficient to…

Information Theory · Computer Science 2019-03-19 Wei Cui , Xu Zhang , Yulong Liu

Context: Statistical properties of the cosmic density fields are to a large extent encoded in the shape of the one-point density probability distribution functions (PDF). In order to successfully exploit such observables, a detailed…

Cosmology and Nongalactic Astrophysics · Physics 2022-07-20 Francis Bernardeau

Two-point correlation functions (2PCF) are widely used to characterize how points cluster in space. In this work, we study the problem of measuring the 2PCF over a large set of points, restricted to a subset satisfying a property of…

Computer Vision and Pattern Recognition · Computer Science 2026-04-08 Max Hamilton , Daniel Sheldon , Subhransu Maji

This paper investigates a statistical procedure for testing the equality of two independent estimated covariance matrices when the number of potentially dependent data vectors is large and proportional to the size of the vectors, that is,…

Statistics Theory · Mathematics 2020-06-01 Rémy Mariétan , Stephan Morgenthaler

This paper considers the problem of robustly estimating a structured covariance matrix with an elliptical underlying distribution with known mean. In applications where the covariance matrix naturally possesses a certain structure, taking…

Applications · Statistics 2016-06-29 Ying Sun , Prabhu Babu , Daniel P. Palomar

Using random matrix technique we determine an exact relation between the eigenvalue spectrum of the covariance matrix and of its estimator. This relation can be used in practice to compute eigenvalue invariants of the covariance…

Statistical Mechanics · Physics 2010-01-15 Z. Burda , A. Goerlich , A. Jarosz , J. Jurkiewicz

We present a method for estimating sparse high-dimensional inverse covariance and partial correlation matrices, which exploits the connection between the inverse covariance matrix and linear regression. The method is a two-stage estimation…

Machine Learning · Statistics 2025-05-13 Samuel Erickson , Tobias Rydén

In this self-contained chapter, we revisit a fundamental problem of multivariate statistics: estimating covariance matrices from finitely many independent samples. Based on massive Multiple-Input Multiple-Output (MIMO) systems we illustrate…

Statistics Theory · Mathematics 2021-06-14 Johannes Maly , Tianyu Yang , Sjoerd Dirksen , Holger Rauhut , Giuseppe Caire

We investigate simulation-based bandpower covariance matrices commonly used in cosmological parameter inferences such as the estimation of the tensor-to-scalar ratio $r$. We find that upper limits on $r$ can be biased low by tens of…

Cosmology and Nongalactic Astrophysics · Physics 2022-07-06 Dominic Beck , Ari Cukierman , W. L. Kimmy Wu

We construct a cosmological toy model in which a step-function ``cosmological constant'' is taken into consideration beside ordinary matter. We assume that $\Lambda$ takes two values depending on the epoch, and matter goes from a radiation…

Astrophysics · Physics 2008-11-26 Salvatore Capozziello , Ruggiero de Ritis , Alma Angela Marino

The systematic magnification of background sources by the weak gravitational-lensing effects of foreground matter, also called cosmic magnification, is becoming an efficient tool both for measuring cosmological parameters and for exploring…

Astrophysics · Physics 2015-06-24 Brice Menard , Takashi Hamana , Matthias Bartelmann , Naoki Yoshida

Ground and space-based sky surveys enable powerful cosmological probes based on measurements of galaxy properties and the distribution of galaxies in the Universe. These probes include weak lensing, baryon acoustic oscillations, abundance…

A novel method is proposed for detecting changes in the covariance structure of moderate dimensional time series. This non-linear test statistic has a number of useful properties. Most importantly, it is independent of the underlying…

Methodology · Statistics 2021-08-18 Sean Ryan , Rebecca Killick