statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.loo_likelihood KDEMultivariateConditional.loo_likelihood(bw, func= at 0x2ac6543b0488>) [source] Returns the leave-one-out conditional likelihood of the data. If func is not equal to the default, what?s calculated is a function of the leave-one-out conditional likelihood. Parameters: bw: array_like : The bandwidth parameter(s). func: callable, optional : Function to transform the likelihood values (before summing); for

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.imse KDEMultivariateConditional.imse(bw) [source] The integrated mean square error for the conditional KDE. Parameters: bw: array_like : The bandwidth parameter(s). Returns: CV: float : The cross-validation objective function. Notes For more details see pp. 156-166 in [R15]. For details on how to handle the mixed variable types see [R16]. The formula for the cross-validation objective function for mixed variable type

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.cdf KDEMultivariateConditional.cdf(endog_predict=None, exog_predict=None) [source] Cumulative distribution function for the conditional density. Parameters: endog_predict: array_like, optional : The evaluation dependent variables at which the cdf is estimated. If not specified the training dependent variables are used. exog_predict: array_like, optional : The evaluation independent variables at which the cdf is estimated

statsmodels.nonparametric.kernel_density.KDEMultivariate.pdf KDEMultivariate.pdf(data_predict=None) [source] Evaluate the probability density function. Parameters: data_predict: array_like, optional : Points to evaluate at. If unspecified, the training data is used. Returns: pdf_est: array_like : Probability density function evaluated at data_predict. Notes The probability density is given by the generalized product kernel estimator:

statsmodels.nonparametric.kernel_density.KDEMultivariate.loo_likelihood KDEMultivariate.loo_likelihood(bw, func= at 0x2ac6543ac488>) [source] Returns the leave-one-out likelihood function. The leave-one-out likelihood function for the unconditional KDE. Parameters: bw: array_like : The value for the bandwidth parameter(s). func: callable, optional : Function to transform the likelihood values (before summing); for the log likelihood, use func=np.log. Default is f(x) = x. Notes The l

statsmodels.nonparametric.kernel_density.KDEMultivariate.imse 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 [R6] for details on how to handle the multivariate estimation with mixed data types see p.6 in [R7]. The formula for the cross-validation objective function is: Where is the multivar

statsmodels.nonparametric.kernel_density.KDEMultivariate.cdf KDEMultivariate.cdf(data_predict=None) [source] Evaluate the cumulative distribution function. Parameters: data_predict: array_like, optional : Points to evaluate at. If unspecified, the training data is used. Returns: cdf_est: array_like : The estimate of the cdf. Notes See http://en.wikipedia.org/wiki/Cumulative_distribution_function For more details on the estimation see Ref. [5] in module docstring. The multivariate CDF

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.Z classmethod KalmanFilter.Z(r) [source] Returns the Z selector matrix in the observation equation. Parameters: r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. Notes Currently only returns a 1 x r vector [1,0,0,...0]. Will need to be generalized when the Kalman Filter becomes more flexible.

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.T classmethod KalmanFilter.T(params, r, k, p) [source] The coefficient matrix for the state vector in the state equation. Its dimension is r+k x r+k. Parameters: r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. k : int The number of exogenous variables in the ARMA model, including the constant if appropriate. p : int The AR coefficient in an ARMA model. References Durbin and Koopm

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.R classmethod KalmanFilter.R(params, r, k, q, p) [source] The coefficient matrix for the state vector in the observation equation. Its dimension is r+k x 1. Parameters: r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. k : int The number of exogenous variables in the ARMA model, including the constant if appropriate. q : int The MA order in an ARMA model. p : int The AR order in an