statistics-0.13.2.3: A library of statistical types, data, and functions

Copyright | (c) 2009 Bryan O'Sullivan
License | BSD3
Maintainer | bos@serpentine.com
Stability | experimental
Portability | portable
Safe Haskell | None
Language | Haskell98

Statistics.Sample.KernelDensity.Simple

Contents

- Simple entry points
- Building blocks
  - Choosing points from a sample
  - Bandwidth estimation
  - Kernels
  - Low-level estimation
- References

Description

Deprecated: Use Statistics.Sample.KernelDensity instead.

Kernel density estimation code, providing non-parametric ways to estimate the probability density function of a sample.

The techniques used by functions in this module are relatively fast, but they generally give inferior results to the KDE function in the main `KernelDensity` module (due to the oversmoothing documented for `bandwidth` below).

Synopsis

- epanechnikovPDF :: Vector v Double => Int -> v Double -> (Points, Vector Double)
- gaussianPDF :: Vector v Double => Int -> v Double -> (Points, Vector Double)
- newtype Points = Points {
  - fromPoints :: Vector Double
  }
- choosePoints :: Vector v Double => Int -> Double -> v Double -> Points
- type Bandwidth = Double
- bandwidth :: Vector v Double => (Double -> Bandwidth) -> v Double -> Bandwidth
- epanechnikovBW :: Double -> Bandwidth
- gaussianBW :: Double -> Bandwidth
- type Kernel = Double -> Double -> Double -> Double -> Double
- epanechnikovKernel :: Kernel
- gaussianKernel :: Kernel
- estimatePDF :: Vector v Double => Kernel -> Bandwidth -> v Double -> Points -> Vector Double
- simplePDF :: Vector v Double => (Double -> Double) -> Kernel -> Double -> Int -> v Double -> (Points, Vector Double)

# Simple entry points

epanechnikovPDF Source

Arguments

:: Vector v Double |
=> Int | Number of points at which to estimate
-> v Double | Data sample
-> (Points, Vector Double) |

Simple Epanechnikov kernel density estimator. Returns the uniformly spaced points from the sample range at which the density function was estimated, and the estimates at those points.

gaussianPDF Source

Arguments

:: Vector v Double |
=> Int | Number of points at which to estimate
-> v Double | Data sample
-> (Points, Vector Double) |

Simple Gaussian kernel density estimator. Returns the uniformly spaced points from the sample range at which the density function was estimated, and the estimates at those points.

# Building blocks

## Choosing points from a sample

newtype Points Source

Points from the range of a `Sample`.

Constructors

Points |

Fields

fromPoints :: Vector Double

Instances

Eq Points Source |
Data Points Source |
Read Points Source |
Show Points Source |
Generic Points Source |
ToJSON Points Source |
FromJSON Points Source |
Binary Points Source |
type Rep Points Source |

choosePoints Source

Arguments

:: Vector v Double |
=> Int | Number of points to select, n
-> Double | Sample bandwidth, h
-> v Double | Input data
-> Points |

Choose a uniform range of points at which to estimate a sample's probability density function.

If you are using a Gaussian kernel, multiply the sample's bandwidth by 3 before passing it to this function.

If this function is passed an empty vector, it returns values of positive and negative infinity.

## Bandwidth estimation

type Bandwidth = Double Source

The width of the convolution kernel used.

bandwidth :: Vector v Double => (Double -> Bandwidth) -> v Double -> Bandwidth Source

Compute the optimal bandwidth from the observed data for the given kernel.

This function uses an estimate based on the standard deviation of a sample (due to Deheuvels), which performs reasonably well for unimodal distributions but leads to oversmoothing for more complex ones.

epanechnikovBW :: Double -> Bandwidth Source

Bandwidth estimator for an Epanechnikov kernel.

gaussianBW :: Double -> Bandwidth Source

Bandwidth estimator for a Gaussian kernel.

## Kernels

type Kernel = Double -> Double -> Double -> Double -> Double Source

The convolution kernel. Its parameters are as follows:

- Scaling factor, 1/nh
- Bandwidth, h
- A point at which to sample the input, p
- One sample value, v

epanechnikovKernel :: Kernel Source

Epanechnikov kernel for probability density function estimation.

gaussianKernel :: Kernel Source

Gaussian kernel for probability density function estimation.

## Low-level estimation

estimatePDF Source

Arguments

:: Vector v Double |
=> Kernel | Kernel function
-> Bandwidth | Bandwidth, h
-> v Double | Sample data
-> Points | Points at which to estimate
-> Vector Double |

Kernel density estimator, providing a non-parametric way of estimating the PDF of a random variable.

simplePDF Source

Arguments

:: Vector v Double |
=> (Double -> Double) | Bandwidth function
-> Kernel | Kernel function
-> Double | Bandwidth scaling factor (3 for a Gaussian kernel, 1 for all others)
-> Int | Number of points at which to estimate
-> v Double | sample data
-> (Points, Vector Double) |

A helper for creating a simple kernel density estimation function with automatically chosen bandwidth and estimation points.

# References

- Deheuvels, P. (1977) Estimation non paramétrique de la densité par histogrammes généralisés. Mhttp:/archive.numdam.orgarticle/RSA_1977__25_3_5_0.pdf>