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ProbDistUnivKernel - Class: ProbDistUnivKernel

Construct ProbDistUnivKernel object

Syntax

PD = ProbDistUnivKernel(X)
PD = ProbDistUnivKernel(X, param1, val1, param2, val2, ...)

Description

PD = ProbDistUnivKernel(X) creates PD, a ProbDistUnivKernel object, which represents a nonparametric probability distribution, based on a normal kernel smoothing function.

PD = ProbDistUnivKernel(X, param1, val1, param2, val2, ...) specifies optional parameter name/value pairs, as described in the Parameter/Values table. Parameter and value names are case insensitive.

Input Arguments

X

A column vector of data.

    Note   Any NaN values in X are ignored by the fitting calculations.

ParameterValues
'censoring'

A Boolean vector the same size as X, containing 1s when the corresponding elements in X are right-censored observations and 0s when the corresponding elements are exact observations. Default is a vector of 0s.

    Note   Any NaN values in this censoring vector are ignored by the fitting calculations.

'frequency'

A vector the same size as X, containing nonnegative integers specifying the frequencies for the corresponding elements in X. Default is a vector of 1s.

    Note   Any NaN values in this frequency vector are ignored by the fitting calculations.

'kernel'

A string specifying the type of kernel smoother to use. Choices are:

  • 'normal' (default)

  • 'box'

  • 'triangle'

  • 'epanechnikov'

'support'

Any of the following to specify the support:

  • 'unbounded' — Default. If the density can extend over the whole real line.

  • 'positive' — To restrict it to positive values.

  • A two-element vector giving finite lower and upper limits for the support of the density.

'width'

A value specifying the bandwidth of the kernel smoothing window. The default is optimal for estimating normal densities, but you may want to choose a smaller value to reveal features such as multiple modes.

Output Arguments

PD

An object in the ProbDistUnivKernel class, which is derived from the ProbDist class. It represents a nonparametric probability distribution.

References

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

See Also

fitdist | ksdensity

  

 

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