# kde: One-dimensional kernel density estimate In pmpp: Posterior Mean Panel Predictor

## Description

State-of-the-art gaussian kernel density estimator for one-dimensional data. The estimator does not use the commonly employed 'gaussian rule of thumb'. As a result, it outperforms many plug-in methods on multimodal densities with widely separated modes. This function is the cleaned-up version of the code written and published by Z. I. Botev at: http://web.maths.unsw.edu.au/~zdravkobotev/

## Usage

 `1` ```kde(data, n, MIN, MAX) ```

## Arguments

 `data` a vector of data from which the density estimate is constructed; `n` the number of mesh points used in the uniform discretization of the interval [MIN, MAX]; n has to be a power of two; if n is not a power of two, then n is rounded up to the next power of two; the default value of n is n=2 ^ 12; `MIN` minimum of the interval [MIN, MAX] on which the density estimate is constructed; default value: MIN = min(data) - Range / 10 `MAX` maximum of the interval [MIN, MAX] on which the density estimate is constructed; default value: MAX = max(data) + Range / 10

## Value

A `matrix` with two rows of length `n`, where the second row contains the density values on the mesh in the first row.

## References

Z. I. Botev, J. F. Grotowski and D. P. Kroese, "Kernel Density Estimation Via Diffusion", Annals of Statistics, 2010, Volume 38, Number 5, Pages 2916-2957

## Examples

 ```1 2 3 4``` ```set.seed(1) data <- c(rnorm(10 ^ 3), rnorm(10 ^ 3) * 2 + 30) d <- kde(data) plot(d[1,], d[2,], type = 'l', xlab = 'x', ylab = 'density f(x)') ```

### Example output ```
```

pmpp documentation built on Oct. 30, 2019, 11:35 a.m.