# statsmodels.nonparametric.kernel_density.KDEMultivariate.imse¶

method

KDEMultivariate.imse(bw)[source]

Returns the Integrated Mean Square Error for the unconditional KDE.

Parameters
bw: array_like

The bandwidth parameter(s).

Returns
CV: float

The cross-validation objective function.

Notes

See p. 27 in [1] for details on how to handle the multivariate estimation with mixed data types see p.6 in [2].

The formula for the cross-validation objective function is:

$CV=\frac{1}{n^{2}}\sum_{i=1}^{n}\sum_{j=1}^{N} \bar{K}_{h}(X_{i},X_{j})-\frac{2}{n(n-1)}\sum_{i=1}^{n} \sum_{j=1,j\neq i}^{N}K_{h}(X_{i},X_{j})$

Where $$\bar{K}_{h}$$ is the multivariate product convolution kernel (consult [2] for mixed data types).

References

1(1,2)

Racine, J., Li, Q. Nonparametric econometrics: theory and practice. Princeton University Press. (2007)

2(1,2,3)

Racine, J., Li, Q. “Nonparametric Estimation of Distributions with Categorical and Continuous Data.” Working Paper. (2000)