npksum
(Package: np) :
Kernel Sums with Mixed Data Types
npksum computes kernel sums on evaluation data, given a set of training data, data to be weighted (optional), and a bandwidth specification (any bandwidth object).
b.star
(Package: np) :
Compute Optimal Block Length for Stationary and Circular Bootstrap
b.star is a function which computes the optimal block length for the continuous variable data using the method described in Patton, Politis and White (2009).
npcdist
(Package: np) :
Kernel Conditional Distribution Estimation with Mixed Data Types
npcdist computes kernel cumulative conditional distribution estimates on p+q-variate evaluation data, given a set of training data (both explanatory and dependent) and a bandwidth specification (a condbandwidth object or a bandwidth vector, bandwidth type, and kernel type) using the method of Li and Racine (2008) and Li, Lin, and Racine (2013). The data may be continuous, discrete (unordered and ordered factors), or some combination thereof.
npcdens
(Package: np) :
Kernel Conditional Density Estimation with Mixed Data Types
npcdens computes kernel conditional density estimates on p+q-variate evaluation data, given a set of training data (both explanatory and dependent) and a bandwidth specification (a conbandwidth object or a bandwidth vector, bandwidth type, and kernel type) using the method of Hall, Racine, and Li (2004). The data may be continuous, discrete (unordered and ordered factors), or some combination thereof.
npregiv computes nonparametric estimation of an instrumental regression function phi defined by conditional moment restrictions stemming from a structural econometric model: E [Y - phi (Z,X) | W ] = 0, and involving endogenous variables Y and Z and exogenous variables X and instruments W. The function phi is the solution of an ill-posed inverse problem.
npindexbw
(Package: np) :
Semiparametric Single Index Model Parameter and Bandwidth Selection
npindexbw computes a npindexbw bandwidth specification using the model Y = G(XB) + epsilon. For continuous Y, the approach is that of Hardle, Hall and Ichimura (1993) which jointly minimizes a least-squares cross-validation function with respect to the parameters and bandwidth. For binary Y, a likelihood-based cross-validation approach is employed which jointly maximizes a likelihood cross-validation function with respect to the parameters and bandwidth. The bandwidth object contains parameters for the single index model and the (scalar) bandwidth for the index function.
npscoefbw computes a bandwidth object for a smooth coefficient kernel regression estimate of a one (1) dimensional dependent variable on p+q-variate explanatory data, using the model Y_i = t(W_i) * gamma(Z_i) + u_i where t(W_i) = (1,t(X_i)) given training points (consisting of explanatory data and dependent data), and a bandwidth specification, which can be a rbandwidth object, or a bandwidth vector, bandwidth type and kernel type.
npregbw
(Package: np) :
Kernel Regression Bandwidth Selection with Mixed Data Types
npregbw computes a bandwidth object for a p-variate kernel regression estimator defined over mixed continuous and discrete (unordered, ordered) data using expected Kullback-Leibler cross-validation, or least-squares cross validation using the method of Racine and Li (2004) and Li and Racine (2004).
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