6.5.5. statsmodels.sandbox.nonparametric.kernel_extras

Multivariate Conditional and Unconditional Kernel Density Estimation with Mixed Data Types

6.5.5.1. 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

6.5.5.2. Functions

gpke(bw, data, data_predict, var_type[, ...]) Returns the non-normalized Generalized Product Kernel Estimator

6.5.5.3. Classes

KDEMultivariate(data, var_type[, bw, defaults]) Multivariate kernel density estimator.
KernelReg(endog, exog, var_type[, reg_type, ...]) Nonparametric kernel regression class.
LeaveOneOut(X) Generator to give leave-one-out views on X.
SemiLinear(endog, exog, exog_nonparametric, ...) Semiparametric partially linear model, Y = Xb + g(Z) + e.
SingleIndexModel(endog, exog, var_type) Single index semiparametric model y = g(X * b) + e.
TestFForm(endog, exog, bw, var_type, fform, ...) Nonparametric test for functional form.