# kde

## Description

`[`

estimates a probability density function (pdf) for the univariate data in the vector
`f`

,`xf`

] = kde(`a`

)`a`

and returns values `f`

of the estimated pdf at
the evaluation points `xf`

. `kde`

uses kernel
density estimation to estimate the pdf. See Kernel Distribution for more
information.

`[___] = kde(`

specifies options using one or more name-value arguments. For example,
`a`

,`Name=Value`

)`kde(a,ProbabilityFcn="cdf")`

estimates the cumulative distribution
function (cdf) for `a`

instead of the pdf. Use this syntax with any of
the output argument combinations in the previous syntaxes.

## Examples

### Estimate Probability Functions

Generate some normally distributed data.

rng(0,"twister") % For reproducibility a = randn(100,1);

Estimate the pdf for the sample data.

[fp,xfp] = kde(a);

`fp`

contains the values for the estimated pdf at the evaluation points in `xfp`

.

Estimate the cdf for the sample data.

`[fc,xfc] = kde(a,ProbabilityFcn="cdf");`

`fc`

contains the values for the estimated cdf at the evaluation points in `xfc`

. `xfc`

and `xfp`

contain the same evaluation points because they were both calculated with the sample data in `a`

.

Evaluate the pdf and cdf for the normal distribution at the evaluation points.

np = (1/sqrt(2*pi))*exp(-.5*(xfp.^2)); nc = 0.5*(1+erf(xfc/sqrt(2)));

Plot the estimated pdf with the normal distribution pdf.

plot(xfp,fp,"-",xfp,np,"--") legend("kde estimate","Normal density")

Plot the estimated pdf with the normal distribution pdf.

figure plot(xfc,fc,"-",xfc,nc,"--") legend("kde estimate","Normal cumulative",Location="northwest")

The plots show that the estimated pdf and cdf have shapes similar to the pdf and cdf of the standard normal distribution.

### Inspect Bandwidth

Generate some normally distributed data.

rng(0,"twister") % For reproducibility a = randn(100,1);

Estimate the pdf for the sample data. By default, `kde`

uses the normal-approximation method to calculate the bandwidth for the kernel smoothing function.

[fn,xfn,bwn] = kde(a);

`fn`

contains the values for the estimated pdf at the evaluation points in `xfn`

, and `bwn`

is the bandwidth for the kernel smoothing function.

Estimate the pdf using the plug-in method, and display the bandwidth associated with each estimated pdf.

```
[p,xp,bwp] = kde(a,Bandwidth="plug-in");
[bwn,bwp]
```

`ans = `*1×2*
0.4958 0.5751

The bandwidth calculated with the normal-approximation method is less than the bandwidth calculated with the plug-in method.

Plot the estimated pdfs.

plot(xfn,fn) hold on plot(xp,p) legend("normal-approx","plug-in")

The estimated pdfs have shapes typical of a normal distribution. The peak of the pdf corresponding to the normal-approximation method is higher than the peak of the pdf corresponding to the plug-in method.

### Compare Kernel Smoothers

Generate some bimodal sample data.

rng(0,"twister") % For reproducibility a = [randn(100,1)-5; randn(20,1)+5];

Use the default `"normal"`

kernel smoothing function to estimate the pdf for the sample data. Use the `"box"`

, `"triangle"`

, and `"parabolic"`

kernel smoothing functions to calculate three more estimates for the pdf.

[f1,xf1] = kde(a); [f2,xf2] = kde(a,Kernel="box"); [f3,xf3] = kde(a,Kernel="triangle"); [f4,xf4] = kde(a,Kernel="parabolic");

`xf1`

, `xf2`

, `xf3`

, and `xf4`

contain the same evaluation points because they were each calculated with the sample data in `a`

. `f1`

, `f2`

, `f3`

, and `f4`

contain the values of each estimated pdf at the evaluation points.

Plot the estimated pdfs.

tiledlayout(2,2) nexttile plot(xf1,f1) % normal nexttile plot(xf2,f2) % box nexttile plot(xf3,f3) % triangle nexttile plot(xf4,f4) % parabolic

The plots show that the four estimated pdfs have similar vertical ranges and two peaks each. The pdf calculated with the `"box"`

kernel appears to be the least smooth of the four estimates.

## Input Arguments

`a`

— Sample data

numeric vector

Sample data used to estimate the probability function, specified as a numeric vector.

**Data Types: **`single`

| `double`

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

**Example: **`kde(a,Kernel="box",Bandwidth=0.8,Weight=wgt)`

specifies a box
kernel smoothing function with a bandwidth of `0.8`

and vector of
observation weights `wgt`

.

`Bandwidth`

— Bandwidth for kernel smoothing function

`"normal-approx"`

(default) | `"plug-in"`

| positive scalar

Bandwidth for the kernel smoothing function, specified as
`"normal-approx"`

, `"plug-in"`

, or a positive
scalar.

When

`Bandwidth`

is`"normal-approx"`

,`kde`

uses the normal-approximation method, or*Silverman's rule of thumb*, to calculate the bandwidth.When

`Bandwidth`

is`"plug-in"`

,`kde`

uses the improved plug-in method described in [1] to calculate the bandwidth. The plug-in method is sometimes called the*Sheather-Jones*method.When

`Bandwidth`

is a positive scalar, its value controls the smoothness of the probability function estimate. As the value increases, the probability function estimate gets smoother.

To see how `Bandwidth`

affects the kernel smoothing function,
see `Kernel`

.

**Example: **`kde(a,Bandwidth="plug-in")`

**Data Types: **`single`

| `double`

| `string`

| `char`

`EvaluationPoints`

— Points at which to evaluate estimated probability function

numeric vector

Points at which to evaluate the estimated probability function, specified as a
numeric vector. By default, `kde`

evaluates the estimated
probability function at `NumPoints`

evenly spaced points that cover
the range of the observations in `a`

.

If you specify both the `NumPoints`

and
`EvaluationPoints`

name-value arguments,
`kde`

ignores `NumPoints`

.

**Example: **`kde(a,EvaluationPoints=linspace(0,10,50))`

**Data Types: **`single`

| `double`

`Kernel`

— Type of kernel smoothing function

`"normal"`

(default) | `"box"`

| `"triangle"`

| `"parabolic"`

| function handle

Type of kernel smoothing function, specified as a function handle or one of the values in this table.

Value | Equation |
---|---|

`"normal"` | $${K}_{i}(x)=\frac{1}{\sqrt{2\pi}}{e}^{\frac{-{d}_{i}^{2}}{2}}$$ |

`"box"` | $${K}_{i}(x)=\{\begin{array}{c}\frac{1}{2\sqrt{3}},\left|{d}_{i}\right|\le \sqrt{3}\\ 0,\left|{d}_{i}\right|>\sqrt{3}\end{array}$$ |

`"triangle"` | $${K}_{i}(x)=\{\begin{array}{c}\frac{1-\frac{\left|{d}_{i}\right|}{\sqrt{6}}}{\sqrt{6}},\left|{d}_{i}\right|\le \sqrt{6}\\ 0,\left|{d}_{i}\right|>\sqrt{6}\end{array}$$ |

`"parabolic"` | $$\begin{array}{l}{K}_{i,h}(x)=\mathrm{max}(0,\frac{3}{4}u),\\ u=\frac{1-\frac{{z}^{2}}{5}}{\sqrt{5}},\\ z=\mathrm{max}(-\sqrt{5},\mathrm{min}({d}_{i},\sqrt{5}))\end{array}$$ |

In the table, $${d}_{i}=\frac{x-{a}_{i}}{h}$$, where *h* is the bandwidth specified in the
`Bandwidth`

name-value argument, and
`a`

is the element at position
_{i}`i`

in `a`

. A random variable with a pdf defined
by one of the kernels in the table has a variance of `1`

. A parabolic
kernel smoothing function is sometimes called an *Epanechnikov*
smoothing function.

If you specify `Kernel`

as a function handle, the function must
accept a matrix or column vector of arbitrary length as its only input argument and
return a nonnegative matrix or vector of the same size.

For more information about how `kde`

uses the kernel
smoothing function to estimate the probability function, see Kernel Distribution.

**Example: **`kde(a,Kernel="parabolic")`

**Data Types: **`string`

| `char`

| `function_handle`

`NumPoints`

— Number of evaluation points

positive integer scalar

Number of evaluation points for the estimated probability function, specified as a
positive integer scalar. By default, `NumPoints = max(100,u)`

, where `u`

is the square root of the number of
elements in `a`

, rounded to the nearest integer.

If you specify both the `NumPoints`

and
`EvaluationPoints`

name-value arguments,
`kde`

ignores `NumPoints`

.

**Example: **`kde(a,NumPoints=100)`

**Data Types: **`single`

| `double`

`ProbabilityFcn`

— Probability function

`"pdf"`

(default) | `"cdf"`

Probability function to estimate, specified as `"pdf"`

or
`"cdf"`

. When `ProbabilityFcn`

is
`"pdf"`

, `kde`

estimates a probability
density function. To estimate a cumulative distribution function, specify
`ProbabilityFcn`

as `"cdf"`

.

**Example: **`kde(a,ProbabilityFcn="cdf")`

`Support`

— Interval for sample data

`"unbounded"`

(default) | `"positive"`

| `"nonnegative"`

| `"negative"`

| two-element numeric vector

Interval for the sample data, specified as a two-element numeric vector,
`"unbounded"`

, `"positive"`

,
`"nonnegative"`

, or `"negative"`

. The elements of
`a`

must be in the interval specified by
`Support`

. The estimated probability function evaluates to
`0`

outside of the interval.

If you specify `Support`

as a two-element vector ```
[L
U]
```

or `[L;U]`

, `L`

must be greater than
`max(a)`

and `U`

must be less than
`min(a)`

. The interval is open with lower bound
`L`

and upper bound `U`

.

If you specify `Support`

as a string, the sample data exists
inside an interval described in this table.

Value | Support |
---|---|

`"unbounded"` | $$(-Inf,Inf)$$ |

`"positive"` | $$(0,Inf)$$ |

`"nonnegative"` | $$[0,Inf)$$ |

`"negative"` | $$(-Inf,0)$$ |

**Example: **`kde(a,Support="nonnegative")`

**Data Types: **`single`

| `double`

| `string`

| `char`

`Weight`

— Observation weights

nonnegative vector

Observation weights, specified as a nonnegative vector. By default,
`kde`

weights all observations in `a`

equally. For more information about how `kde`

uses weights
to estimate the probability function, see Kernel Distribution.

**Data Types: **`single`

| `double`

## Output Arguments

`f`

— Estimated function values

numeric vector

Estimated function values, returned as a numeric vector. The length of
`f`

is equal to the number of evaluation points in
`xf`

.

`xf`

— Evaluation points

numeric vector

Evaluation points, returned as a numeric vector. `xf`

has the
same size as the `EvaluationPoints`

name-value argument, if
`EvaluationPoints`

is specified. Otherwise, the size of
`xf`

is given by the `NumPoints`

name-value
argument.

`bw`

— Bandwidth

positive scalar

Bandwidth for the kernel smoothing function, returned as a positive scalar. You can
use the `Bandwidth`

name-value argument to specify the value for
`bw`

or the method for calculating `bw`

.

## More About

### Kernel Distribution

A kernel distribution is a nonparametric representation of a probability density function (pdf) of a random variable. You can use a kernel distribution when a parametric distribution cannot properly describe the data or when you want to avoid making assumptions about the distribution of the data. A kernel distribution is defined by a smoothing function and a bandwidth value, which control the smoothness of the resulting density curve.

The kernel estimator is an estimated probability
function for a random variable. For any real values of *x*, the kernel
estimator for the pdf is given by

$${\widehat{f}}_{h}\left(x\right)=\frac{1}{nh}{\displaystyle \sum _{i=1}^{n}{w}_{i}K\left(\frac{x-{x}_{i}}{h}\right)}\text{\hspace{0.17em}},$$

where the *x _{i}* values are random
samples from an unknown distribution,

*w*values are their corresponding weights,

_{i}*n*is the sample size, $$K$$ is the kernel smoothing function, and

*h*is the bandwidth.

For any real values of *x*, the kernel estimator for the cumulative
distribution function (cdf) is given by

$${\widehat{F}}_{h}\left(x\right)={\displaystyle {\int}_{-\infty}^{x}{\widehat{f}}_{h}(t)dt}=\frac{1}{nh}{\displaystyle \sum _{i=1}^{n}{w}_{i}G\left(\frac{x-{x}_{i}}{h}\right)}\text{\hspace{0.17em}},$$

where $$G(x)={\displaystyle {\int}_{-\infty}^{x}K(t)dt}$$.

For more details, see Kernel Distribution (Statistics and Machine Learning Toolbox).

## References

[1] Botev, Z. I.,
J. F. Grotowski, and D. P. Kroese. "Kernel Density Estimation via Diffusion." *The
Annals of Statistics*, vol. 38, no. 5 (October 1, 2010). https://projecteuclid.org/journals/annals-of-statistics/volume-38/issue-5/Kernel-density-estimation-via-diffusion/10.1214/10-AOS799.full

[2] Bowman, A. W., and A. Azzalini. "Applied Smoothing Techniques for Data Analysis." New York: Oxford University Press Inc., 1997.

[3] Hill, P. D. "Kernel estimation of
a distribution function." *Communications in Statistics - Theory and
Methods*. 14, no. 3(January 1985): 605–620.

[4] Jones, M. C. "Simple boundary
correction for kernel density estimation." *Statistics and Computing*. no.
3(September 1993): 135–146.

[5] Silverman, B. W. "Density Estimation for Statistics and Data Analysis." Chapman & Hall/CRC, 1986.

## Version History

**Introduced in R2023b**

## See Also

### Functions

`histogram`

|`histcounts`

(Statistics and Machine Learning Toolbox) |`ksdensity`

(Statistics and Machine Learning Toolbox)

### Topics

- Kernel Distribution (Statistics and Machine Learning Toolbox)
- Nonparametric and Empirical Probability Distributions (Statistics and Machine Learning Toolbox)

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