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Lightweight robust covariance estimation in Julia i.e. if you have a data matrix `X`

of size `n×p`

corresponding to `n`

observations with `p`

features, this package will help you to obtain an estimator of the covariance matrix of size `p×p`

associated with this data.

**Note**: if you are interested in covariance estimation in the context of a linear regression, consider for now the package CovarianceMatrices.jl which focuses around that case.

```
using CovarianceEstimation
X = randn(5, 7)
S_uncorrected = cov(SimpleCovariance(), X)
S_corrected = cov(SimpleCovariance(corrected=true), X)
# using linear shrinkage with different targets
LSE = LinearShrinkage
# - Ledoit-Wolf target + shrinkage
method = LSE(ConstantCorrelation())
S_ledoitwolf = cov(method, X)
# - Chen target + shrinkage (using the more verbose call)
method = LSE(target=DiagonalCommonVariance(), shrinkage=:rblw)
S_chen_rblw = cov(method, X)
method = LSE(target=DiagonalCommonVariance(), shrinkage=:oas)
S_chen_oas = cov(method, X)
# a pre-defined shrinkage can be used as well
method = LinearShrinkage(DiagonalUnitVariance(), 0.5)
# using a given shrinkage
S_05 = cov(method, X)
```

In this section, `X`

is the data matrix of size `n × p`

, `S`

is the sample covariance matrix with `S = κ (Xc' * Xc)`

where `κ`

is either `n`

(uncorrected) or `n-1`

(corrected) and `Xc`

is the centred data matrix (see docs).

`SimpleCovariance`

: basic corrected or uncorrected sample covariance (implemented in`StatsBase.jl`

).

**Time complexity**: `O(p²n)`

with a low constant

These methods build an estimator of the covariance derived from `S`

. They are implemented using abstract covariance estimation interface from `StatsBase.jl`

.

`LinearShrinkage`

: James-Stein type estimator of the form`(1-λ)S+λF`

where`F`

is a target and`λ∈[0,1]`

a shrinkage intensity.- common targets are implemented following the taxonomy given in [
**1**] along with Ledoit-Wolf optimal shrinkage intensities [**2**]. - in the case of the
`DiagonalCommonVariance`

target, a Rao-Blackwellised Ledoit-Wolf shrinkage (`:rblw`

) and Oracle-Approximating shrinkage (`:oas`

) are also supported (see [**3**]). **Note**:`S`

is symmetric semi-positive definite so that if the`F`

is symmetric positive definite and provided`λ`

is non-zero, the estimator obtained after shrinkage is also symmetric positive definite. For the diagonal targets`DiagonalUnitVariance`

,`DiagonalCommonVariance`

and`DiagonalUnequalVariance`

the target is necessarily SPD.

- common targets are implemented following the taxonomy given in [
`AnalyticalNonlinearShrinkage`

: estimator of the form`MΛM'`

where`M`

and`Λ`

are matrices derived from the eigen decomposition of`S`

.[**4**]`WoodburyEstimator`

: estimator of the form`σ²I + U * Λ * U'`

, where`I`

is the identity matrix,`U`

is low rank semi-orthogonal, and`Λ`

is diagonal. This form is well-suited to very high-dimensional problems.[**6**]`BiweightMidcovariance`

: robust estimator, described more in the documentation.[**5**]

**Time complexity**:

- Linear shrinkage:
`O(p²n)`

with a low constant (main cost is forming`S`

) - Nonlinear shrinkage:
- if
`p<n`

:`O(p²n + n²)`

with a moderate constant (main cost is forming`S`

and manipulating a matrix of`n²`

elements) - if
`p>n`

:`O(p³)`

with a low constant (main cost is computing the eigen decomposition of`S`

).

- if
- Biweight midcovariance:
`O(p²n)`

These are estimators that may be implemented in the future, see also this review paper.

- Sparsity based estimators for either the covariance or the precision
- Rank based approaches
- POET
- HAC
- ...

For HAC (and other estimators of covariance of coefficient of regression models) you can currently use the CovarianceMatrices.jl package.

Rough benchmarks are run over random matrices of various sizes (`40x20, 20x40, 400x200, 200x400`

).
These benchmarks should (as usual) be taken with a pinch of salt but essentially a significant speedup should be expected for a standard problem.

**Sklearn**(implements`DiagonalCommonVariance`

target with`oas`

and`lw`

shrinkage)- average speedup:
`5x`

- average speedup:
**Corpcor**(implements`DiagonalUnequalVariance`

target with`ss`

shrinkage)- average speedup:
`22x`

- average speedup:
**Ledoit-Wolf 1**(implements`ConstantCorrelation`

target with`lw`

shrinkage, we used Octave for the comparison)- average speedup:
`12x`

- average speedup:
**Ledoit-Wolf 2**(implements`AnalyticalNonlinearShrinkage`

)- average speedup:
`25x`

- average speedup:
**Astropy**(implements`BiweightMidcovariance`

)- average speedup:
`3x`

- average speedup:

- [
**1**] J. Schäfer and K. Strimmer,*A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics*, Statistical Applications in Genetics and Molecular Biology, 2005. - [
**2**] O. Ledoit and M. Wolf,*Honey, I Shrunk the Sample Covariance Matrix*, The Journal of Portfolio Management, 2004. - [
**3**] Y. Chen, A. Wiesel, Y. C. Eldar, and A. O. Hero,*Shrinkage Algorithms for MMSE Covariance Estimation*, IEEE Transactions on Signal Processing, 2010. - [
**4**] O. Ledoit and M. Wolf,*Analytical Nonlinear Shrinkage of Large-Dimensional Covariance Matrices*, Working Paper, 2018. - [
**5**] Beers, Flynn, and Gebhardt (1990; AJ 100, 32) "Measures of Location and Scale for Velocities in Clusters of Galaxies – A Robust Approach" - [
**6**] Donoho, D.L., Gavish, M. and Johnstone, I.M., 2018.*Optimal shrinkage of eigenvalues in the spiked covariance model.*Annals of Statistics, 46(4), p.1742.