statsmodels.nonparametric.kernel_density.KDEMultivariate¶
- class statsmodels.nonparametric.kernel_density.KDEMultivariate(data, var_type, bw=None, defaults=None)[source]¶
Multivariate kernel density estimator.
This density estimator can handle univariate as well as multivariate data, including mixed continuous / ordered discrete / unordered discrete data. It also provides cross-validated bandwidth selection methods (least squares, maximum likelihood).
- Parameters:
- data
list
of
ndarrays
or 2-Dndarray
The training data for the Kernel Density Estimation, used to determine the bandwidth(s). If a 2-D array, should be of shape (num_observations, num_variables). If a list, each list element is a separate observation.
- var_type
str
The type of the variables:
c : continuous
u : unordered (discrete)
o : ordered (discrete)
The string should contain a type specifier for each variable, so for example
var_type='ccuo'
.- bwarray_like or
str
,optional
If an array, it is a fixed user-specified bandwidth. If a string, should be one of:
normal_reference: normal reference rule of thumb (default)
cv_ml: cross validation maximum likelihood
cv_ls: cross validation least squares
- defaults
EstimatorSettings
instance
,optional
The default values for (efficient) bandwidth estimation.
- data
See also
Examples
>>> import statsmodels.api as sm >>> nobs = 300 >>> np.random.seed(1234) # Seed random generator >>> c1 = np.random.normal(size=(nobs,1)) >>> c2 = np.random.normal(2, 1, size=(nobs,1))
Estimate a bivariate distribution and display the bandwidth found:
>>> dens_u = sm.nonparametric.KDEMultivariate(data=[c1,c2], ... var_type='cc', bw='normal_reference') >>> dens_u.bw array([ 0.39967419, 0.38423292])
- Attributes:
- bwarray_like
The bandwidth parameters.
Methods
cdf
([data_predict])Evaluate the cumulative distribution function.
imse
(bw)Returns the Integrated Mean Square Error for the unconditional KDE.
loo_likelihood
(bw[, func])Returns the leave-one-out likelihood function.
pdf
([data_predict])Evaluate the probability density function.