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- W3122323237 abstract "This paper deals with certain estimation problems involving the covariance matrix in large dimensions. Due to the breakdown of finite-dimensional asymptotic theory when the dimension is not negligible with respect to the sample size, it is necessary to resort to an alternative framework known as large-dimensional asymptotics. Recently, Ledoit and Wolf (2015) have proposed an estimator of the eigenvalues of the population covariance matrix that is consistent according to a mean-square criterion under large-dimensional asymptotics. It requires numerical inversion of a multivariate nonrandom function which they call the QuEST function. The present paper explains how to numerically implement the QuEST function in practice through a series of six successive steps. It also provides an algorithm to compute the Jacobian analytically, which is necessary for numerical inversion by a nonlinear optimizer. Monte Carlo simulations document the effectiveness of the code." @default.
- W3122323237 created "2021-02-01" @default.
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- W3122323237 date "2016-01-01" @default.
- W3122323237 modified "2023-09-27" @default.
- W3122323237 title "Numerical implementation of the QuEST function" @default.
- W3122323237 hasPublicationYear "2016" @default.
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