## Documentation Center |

Jackknife sampling

`jackstat = jackknife(jackfun,X)jackstat = jackknife(jackfun,X,Y,...)jackstat = jackknife(jackfun,...,'Options',option)`

`jackstat = jackknife(jackfun,X)` draws
jackknife data samples from the `n`-by-`p` data
array `X`, computes statistics on each sample using
the function `jackfun`, and returns the results in
the matrix `jackstat`. `jackknife` regards
each row of `X` as one data sample, so there are `n` data
samples. Each of the `n` rows of `jackstat` contains
the results of applying `jackfun` to one jackknife
sample. `jackfun` is a function handle specified
with `@`. Row `i` of `jackstat` contains
the results for the sample consisting of `X` with
the `i`th row omitted:

s = x; s(i,:) = []; jackstat(i,:) = jackfun(s);

If `jackfun` returns
a matrix or array, then this output is converted to a row vector for
storage in `jackstat`. If `X` is
a row vector, it is converted to a column vector.

`jackstat = jackknife(jackfun,X,Y,...)` accepts
additional arguments to be supplied as inputs to `jackfun`.
They may be scalars, column vectors, or matrices. `jackknife` creates
each jackknife sample by sampling with replacement from the rows of
the non-scalar data arguments (these must have the same number of
rows). Scalar data are passed to `jackfun` unchanged.
Non-scalar arguments must have the same number of rows, and each
jackknife sample omits the same row from these arguments.

`jackstat = jackknife(jackfun,...,'Options',option)` provides
an option to perform jackknife iterations in parallel, if the Parallel Computing Toolbox™ is
available. Set `'Options'` as a structure you create
with `statset`. `jackknife` uses
the following field in the structure:

'UseParallel' | If |

Estimate the bias of the MLE variance estimator of random samples
taken from the vector `y` using `jackknife`.
The bias has a known formula in this problem, so you can compare
the `jackknife` value to this formula.

sigma = 5; y = normrnd(0,sigma,100,1); m = jackknife(@var, y, 1); n = length(y); bias = -sigma^2 / n % known bias formula jbias = (n - 1)*(mean(m)-var(y,1)) % jackknife bias estimate bias = -0.2500 jbias = -0.3378

`bootstrp` | `hist` | `ksdensity` | `random` | `randsample`

Was this topic helpful?