bandingEst() estimates the covariance matrix of data with
ordered variables by forcing off-diagonal entries to be zero for indices
that are far removed from one another. The i, j entry of the estimated
covariance matrix will be zero if the absolute value of i - j is greater
than some non-negative constant k. This estimator was proposed by
Bickel and Levina (2008)
.
bandingEst(dat, k)A numeric data.frame, matrix, or similar object.
A non-negative, numeric integer.
A matrix corresponding to the estimate of the covariance
matrix.
Bickel PJ, Levina E (2008). “Regularized estimation of large covariance matrices.” Annals of Statistics, 36(1), 199--227. doi: 10.1214/009053607000000758 .
bandingEst(dat = mtcars, k = 2L)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 36.324103 -9.172379 -633.09721 0.00000 0.00000000 0.0000000
#> [2,] -9.172379 3.189516 199.66028 101.93145 0.00000000 0.0000000
#> [3,] -633.097208 199.660282 15360.79983 6721.15867 -47.06401915 0.0000000
#> [4,] 0.000000 101.931452 6721.15867 4700.86694 -16.45110887 44.1926613
#> [5,] 0.000000 0.000000 -47.06402 -16.45111 0.28588135 -0.3727207
#> [6,] 0.000000 0.000000 0.00000 44.19266 -0.37272073 0.9573790
#> [7,] 0.000000 0.000000 0.00000 0.00000 0.08714073 -0.3054816
#> [8,] 0.000000 0.000000 0.00000 0.00000 0.00000000 -0.2736613
#> [9,] 0.000000 0.000000 0.00000 0.00000 0.00000000 0.0000000
#> [10,] 0.000000 0.000000 0.00000 0.00000 0.00000000 0.0000000
#> [11,] 0.000000 0.000000 0.00000 0.00000 0.00000000 0.0000000
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000
#> [2,] 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000
#> [3,] 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000
#> [4,] 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000
#> [5,] 0.08714073 0.00000000 0.00000000 0.0000000 0.00000000
#> [6,] -0.30548161 -0.27366129 0.00000000 0.0000000 0.00000000
#> [7,] 3.19316613 0.67056452 -0.20495968 0.0000000 0.00000000
#> [8,] 0.67056452 0.25403226 0.04233871 0.0766129 0.00000000
#> [9,] -0.20495968 0.04233871 0.24899194 0.2923387 0.04637097
#> [10,] 0.00000000 0.07661290 0.29233871 0.5443548 0.32661290
#> [11,] 0.00000000 0.00000000 0.04637097 0.3266129 2.60887097