https://github.com/ararslan/jackknife.jl

Jackknife resampling and estimation in Julia
https://github.com/ararslan/jackknife.jl

estimation jackknife julia statistics

Last synced: 10 days ago
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Jackknife resampling and estimation in Julia

• Host: GitHub
• URL: https://github.com/ararslan/jackknife.jl
• Owner: ararslan
• License: mit
• Created: 2016-07-15T00:02:46.000Z (over 8 years ago)
• Default Branch: main
• Last Pushed: 2022-08-09T20:49:47.000Z (about 2 years ago)
• Last Synced: 2024-10-15T18:33:05.551Z (21 days ago)
• Topics: estimation, jackknife, julia, statistics
• Language: Julia
• Size: 27.3 KB
• Stars: 24
• Watchers: 7
• Forks: 8
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Jackknife.jl

[![CI](https://github.com/ararslan/Jackknife.jl/workflows/CI/badge.svg)](https://github.com/ararslan/Jackknife.jl/actions?query=workflow%3ACI+branch%3Amaster)

[![Codecov](http://codecov.io/github/ararslan/Jackknife.jl/coverage.svg?branch=master)](http://codecov.io/github/ararslan/Jackknife.jl?branch=master)

This package provides [jackknife](https://en.wikipedia.org/wiki/Jackknife_resampling)

resampling and estimation functions for Julia.

## Installation

This package is registered in the General registry and can be installed from the Julia

REPL with

```julia

julia> using Pkg

julia> Pkg.add("Jackknife")

```

or in the Pkg REPL mode with `]add Jackknife`.

## Functions

None of the functions here are exported, so you'll have to call them with the prefix

`Jackknife.` or explicitly import them.

Each function takes the following two arguments:

 * A point estimator, given as a `Function`.

   The function must return a scalar when passed a vector.

 * A real-valued vector of length > 1.

### Resampling

```julia

leaveoneout(estimator, x)

```

Compute a vector of point estimates based on systematic subsamples of `x` wherein

each index is omitted one at a time.

These are the "leave-one-out" estimates.

The resulting vector will have length `length(x) - 1`.

### Estimation

```julia

variance(estimator, x)

```

The variance of the estimator computed using the jackknife technique.

```julia

bias(estimator, x)

```

The bias of the estimator computed using the jackknife technique.

```julia

estimate(estimator, x)

```

The bias-corrected jackknife estimate of the parameter.