Banded.Chol: Computes estimate of covariance matrix by banding the... In FastBandChol: Fast Estimation of a Covariance Matrix by Banding the Cholesky Factor

Description

Computes estimate of covariance matrix by banding the Cholesky factor using a modified Gram Schmidt algorithm implemented in RcppArmadillo.

Usage

 `1` ```banded.chol(X, bandwidth, centered = FALSE) ```

Arguments

 `X` A data matrix with n rows and p columns. Rows are assumed to be independent realizations from a p-variate distribution with covariance Σ. `bandwidth` A positive integer. Must be less than n-1 and p-1. `centered` Logical. Is data matrix centered? Default is `centered = FALSE`

Value

A list with

 `est` The estimated covariance matrix.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```## set sample size and dimension n=20 p=100 ## create covariance with AR1 structure Sigma = matrix(0, nrow=p, ncol=p) for(l in 1:p){ for(m in 1:p){ Sigma[l,m] = .5^(abs(l-m)) } } ## simulation Normal data eo1 = eigen(Sigma) Sigma.sqrt = eo1\$vec%*%diag(eo1\$val^.5)%*%t(eo1\$vec) X = t(Sigma.sqrt%*%matrix(rnorm(n*p), nrow=p, ncol=n)) ## compute estimate out1 = banded.chol(X, bandwidth=4) ```

Example output

```
```

FastBandChol documentation built on May 2, 2019, 3:41 a.m.