Numerical evaluation of methods approximating the distribution of a large quadratic form in normal variables

Tong Chen and Thomas Lumley

Computational Statistics & Data Analysis, 2019, vol. 139, issue C, 75-81

Abstract: Quadratic forms of Gaussian variables occur in a wide range of applications in statistics. They can be expressed as a linear combination of chi-squareds. The coefficients in the linear combination are the eigenvalues λ1,…,λn of ΣA, where A is the matrix representing the quadratic form and Σ is the covariance matrix of the Gaussians. The previous literature mostly deals with approximations for small quadratic forms (n<10) and moderate p-values (p>10−2). Motivated by genetic applications, moderate to large quadratic forms (300Keywords: Small p-values; Leading-eigenvalue approximation; Accuracy; Computational complexity (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:139:y:2019:i:c:p:75-81

DOI: 10.1016/j.csda.2019.05.002

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