Kernel smoothing function estimate for multivariate data

computes a probability density estimate of the sample data in the
`f`

= mvksdensity(`x`

,`pts`

,'Bandwidth',`bw`

)*n*-by-*d* matrix `x`

,
evaluated at the points in `pts`

using the required name-value
pair argument value `bw`

for the bandwidth value. The estimation
is based on a product Gaussian kernel function.

For univariate or bivariate data, use `ksdensity`

instead.

returns any of the previous output arguments, using additional options specified by
one or more `f`

= mvksdensity(`x`

,`pts`

,'Bandwidth',`bw`

,`Name,Value`

)`Name,Value`

pair arguments. For example, you can
define the function type that `mvksdensity`

evaluates, such as
probability density, cumulative probability, or survivor function. You can also
assign weights to the input values.

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

[2] Hill, P. D. “Kernel estimation of a distribution function.”
*Communications in Statistics – Theory and Methods*. Vol. 14,
Issue 3, 1985, pp. 605-620.

[3] Jones, M. C. “Simple boundary correction for kernel density
estimation.” *Statistics and Computing*. Vol. 3, Issue 3,
1993, pp. 135-146.

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

[5] Scott, D. W. *Multivariate Density Estimation: Theory, Practice, and
Visualization*. John Wiley & Sons, 2015.