Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

kalmanmain - kfilter2 - kuva
kalmanmain sets defaults for library kalman
kalmantest Tests the quantlets of the kalman library.
kanta Supporting Quantlet for cartsplit
kaplanmeier Calculation of the Kaplan-Meir (product limit) estimator of the hazard rate and the survivor function for a set of durations. The first column of the input is a censorship indicator variable, (equal to zero if the duration is censored, and to one otherwise); the second column is the duration.
kem Calculates estimates of mu, F, Q and R in a state-space model using EM-algorithm. The state-space model is assumed to be in the following form:

y_t = H x_t + v_t

x_t = F x_t-1 + w_t

x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R)

Parameters Sig and H are assumed known.

kemitor Calculates observations of a given state-space model. The state-space model is assumed to be in the following form:

y_t = H x_t + ErrY_t

x_t = F x_t-1 + ErrX_t

x_0 = mu

kemitor2 Simulates observations and states of a given state-space-model - just as kemitor by Petr Franek (quantlib times) - but this time also the states are returned. The state-space model is assumed to be in the following form:

y_t = H x_t + ErrY_t

x_t = F x_t-1 + ErrX_t

x_0 = mu

kernelmain generate the volume of the unit balls
kerneltest kerneltest tests all the aforementioned macros of the kernel.lib
kfilter Calculates a filtered time serie (uni- or multivariate) using the Kalman filter equations. The state-space model is assumed to be in the following form:

y_t = H x_t + v_t

x_t = F x_t-1 + w_t

x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R)

All parameters are assumed known.

kfilter2 Calculates a filtered time serie (uni- or multivariate) using the Kalman filter equations. The state-space model is assumed to be in the following form:

y_t = H x_t + v_t

x_t = F x_t-1 + w_t

x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R)

All parameters are assumed known.

kmcont performes a K-means cluster analysis of the rows of a contingency table including the multivariate graphic using the correspondence analysis; makes available the factorial coordinates (scores)
kmeans performs cluster analysis, i.e. computes a partition of n row points into K clusters.
knn computes a running mean over (2k+1) consecutive values of a given vector. To have the same length at the beginning and at the end the first and last value are repeated k times.
kommumat generates a help matrix for Subset VAR models
kpss Calculation of the KPSS statistics for I(0) against long-memory alternatives. We consider the two tests, denoted by KPSS_mu and KPSS_t, and respectively based on a regression on a constant mu, and on a constant and a time trend t. The quantlet returns the value of the estimated statistic for two type of the tests, i.e., const or trend and the critical values for a 10, 5 and 1 percent confidence interval for I(0) (const, trend).

kpssnum Calculation of the KPSS statistics for I(0) against long-memory alternatives. We consider the two tests, denoted by KPSS_mu and KPSS_t, and respectively based on a regression on a constant mu, and on a constant and a time trend t. The quantlet returns the value of the estimated statistic for two type of the tests, i.e., const or trend and the critical values for a 95 percent confidence interval for I(0) (const, trend).

kron Computes the Kronecker product of two matrices.
ksmoother Calculates a smoothed time serie (uni- or multivariate) using the Kalman smoother equations. The state-space model is assumed to be in the following form:

y_t = H x_t + v_t

x_t = F x_t-1 + w_t

x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R)

All parameters are assumed known.

kurtosis Computes the kurtosis for a given vector.
kuva Makes a picture of a regression tree.

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 17.8.2000