EstimSigma: Data variance estimation

In chickn: 'Compressive' Hierarchical Kernel Clustering Toolbox

Description

The mean data variance estimation.

Usage

EstimSigma(
  Data,
  ind.col,
  m,
  nblocks = 32,
  niter = 3,
  sigma_start = 0.1,
  nparts = 1,
  ...
)

Arguments

Data	A Filebacked Big Matrix n x N. Data signals are stored in the matrix columns.
ind.col	Column indeces for which the data sketch is computed. By default all matrix columns.
m	Number of frequency vectors.
nblocks	Number of blocks, on which the regression is performed. Default is 32.
niter	Number of iterations. Default is 3.
sigma_start	An initial value of the data variance. Default is 0.1.
nparts	Number of parts to split the data for the data sketch computation.
...	Additional arguments passed on to DrawFreq function.

Value

The estimated data variance.

Note

The idea of the variance estimation on the data fraction is taken from \insertRefDBLP:journals/corr/KerivenBGP16chickn.

See Also

DrawFreq, Sketch, GenerateFrequencies

Examples

X = matrix(rnorm(1e5), ncol=1000, nrow = 100)
X_FBM = bigstatsr::FBM(init = X, ncol=1000, nrow = 100)
sigma = EstimSigma(Data = X_FBM, ind.col = seq(1,1000, by = 2), m = 20, nblocks = 4)  

chickn documentation built on Jan. 13, 2021, 10:53 p.m.