https://github.com/twolodzko/kernelboot

Smoothed bootstrap and functions for sampling from kernel densities
https://github.com/twolodzko/kernelboot

bootstrap density kernel-density r random-generation simulation

Last synced: 4 months ago
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Smoothed bootstrap and functions for sampling from kernel densities

• Host: GitHub
• URL: https://github.com/twolodzko/kernelboot
• Owner: twolodzko
• Created: 2016-10-20T19:18:21.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-04-14T10:08:26.000Z (almost 2 years ago)
• Last Synced: 2024-10-17T18:40:33.099Z (4 months ago)
• Topics: bootstrap, density, kernel-density, r, random-generation, simulation
• Language: R
• Homepage:
• Size: 2.89 MB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# kernelboot

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This package implements random generation procedures for sampling from kernel

densities and smoothed bootstrap, that is an extension of standard bootstrap

procedure, where instead of drawing samples with replacement from the empirical

distribution, they are drawn from kernel density estimate of the distribution.

Three functions are provided to sample from univariate kernel densities (`ruvk`),

multivariate product kernel densities (`rmvk`) and multivariate Gaussian kernel

densities (`rmvg`). The `ruvk` function samples from the kernel densities as 

estimated using the base R `density` function. It offers possibility of sampling

from kernel densities with Gaussian, Epanechnikov, rectangular, triangular, biweight,

cosine, and optcosine kernels. The `rmvk` offers sampling from a multivariate kernel

density constructed from independent univariate kernel densities. It is also possible

to sample from multivariate Gaussian kernel density using the `rmvg` function,

that allows for correlation between the variables.

Smooth bootstrap is possible by using the `kernelboot` function, that draws with

replacement samples from the empirical distribution, enhances them using noise

drawn from the kernel density and evaluates the user-provided statistic on the

samples. This procedure can be thought as an extension of the basic bootstrap

procedure.