3.8.5. statsmodels.nonparametric.kernel_density

Multivariate Conditional and Unconditional Kernel Density Estimation with Mixed Data Types.

3.8.5.1. References

[1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice.

Princeton University Press. (2007)

[2] Racine, Jeff. “Nonparametric Econometrics: A Primer,” Foundation

and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008) http://dx.doi.org/10.1561/0800000009

[3] Racine, J., Li, Q. “Nonparametric Estimation of Distributions

with Categorical and Continuous Data.” Working Paper. (2000)

[4] Racine, J. Li, Q. “Kernel Estimation of Multivariate Conditional

Distributions Annals of Economics and Finance 5, 211-235 (2004)

[5] Liu, R., Yang, L. “Kernel estimation of multivariate

cumulative distribution function.” Journal of Nonparametric Statistics (2008)

[6] Li, R., Ju, G. “Nonparametric Estimation of Multivariate CDF

with Categorical and Continuous Data.” Working Paper

[7] Li, Q., Racine, J. “Cross-validated local linear nonparametric

regression” Statistica Sinica 14(2004), pp. 485-512

[8] Racine, J.: “Consistent Significance Testing for Nonparametric

Regression” Journal of Business & Economics Statistics

[9] Racine, J., Hart, J., Li, Q., “Testing the Significance of

Categorical Predictor Variables in Nonparametric Regression Models”, 2006, Econometric Reviews 25, 523-544

3.8.5.2. Functions

gpke(bw, data, data_predict, var_type[, ...])

Returns the non-normalized Generalized Product Kernel Estimator

3.8.5.3. Classes

EstimatorSettings([efficient, randomize, ...])	Object to specify settings for density estimation or regression.
GenericKDE	Base class for density estimation and regression KDE classes.
KDEMultivariate(data, var_type[, bw, defaults])	Multivariate kernel density estimator.
KDEMultivariateConditional(endog, exog, ...)	Conditional multivariate kernel density estimator.
LeaveOneOut(X)	Generator to give leave-one-out views on X.