Kernel smoothing function estimate for multivariate data
computes a probability density estimate of the sample data in the
n-by-d matrix f
= mvksdensity(x
,pts
,'Bandwidth',bw
)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.