evaluates a kernel estimate of an integrated squared density (derivative) using the normal kernel for a (vector of) bandwidth(s) h. This quantlet is a variation of Rdenxest and uses linearly prebinned data for faster computation.
Computes RDL1 estimate --- a weighted L1-estimator of y on on continuous variables x and binary variables xdum with weights min(1, p/(RD^2)); RD contains the robust distances obtained by the MVE estimator for x.
readevent reads a key- or a mouse- event while a program is running. An "event" is a stroke of a key or a click of a mouse button. readevent will be mainly useful for letting XploRe know whether such an event has occured and to get some special information like the coordinates where the mouse click
allocates categories 1,2,...,L to intervals of categories. The upper bounds of the intervals have to be specified. It is an useful tool to join classes and hence to collaps contingency tables.
determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice
computes uniform confidence bands with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
computes pointwise confidence intervals with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice
replicdata reduces a matrix x to its distinct rows and gives the number of replications of each rows in the original dataset. An optional second matrix y can be given, the rows of y are summed up accordingly. replicdata does in fact the same as discrete but provides an additional index vector to id
Calculates a filtered time serie (uni- or multivariate) using a robust, recursive Filter based on LS-optimality, the rLS-filter. Additionally to the Kalman-Filter one needs to specify the degree of robustness one wants to achieve; this is done either by specifying a clipping height or by specifying
Auxiliary routine for rlsfil: solves E [ |X-MYw_b(MY)|^2]=(1+e)E [ |X-MY|^2] - if possible - by MC-integration for X ~ N_n(0,Sigt), v ~ N_m(0,Q) indep. M = Sigt H'(Q+HSigt H')^{-1} Y = HX+v, w_b(x)=min(1,b/|x|)
Auxiliary routine for rlsfil: solves E [ |X-MYw_b(MY)|^2]=(1+e)E [ |X-MY|^2] - if possible - by numerical integration for X ~ N(0,Sigt), v ~ N(0,Q) indep. M=Sigt H'(Q+HSigt H')^{-1} Y=HX+v, w_b(x)=min(1,b/|x|)
Calculates a filtered time serie (uni- or multivariate) using a robust, recursive Filter based on LS-optimality, the rLS-filter. additionally to the Kalman-Filter one needs to specify the degree of robustness one wants to achieve; this is done either by specifying a clipping height or by specifying
Semiparametric average periodogram estimator of the degree of long memory of a time series. The first argument of the quantlet is the series, the second optional argument is a strictly positive constant q, which is also strictly less than one. The third optional argument is the bandwidth vector m.
Semiparametric Gaussian estimator of the degree of long memory of a time series, based on the Whittle estimator. The first argument is the series, the second argument is the vector of bandwidths, i.e., the number of frequencies after zero that are considered. By default, the bandwidth vector m = n/
Computes the regression rankscore test of a linear hypothesis based on the dual quantile regression process. It tests the hypothesis that b1 = 0 in the quantile regression model y = x0'b0 + x1'b1 + u. Test statistic is asymptotically Chi-squared with rank(x1) degrees of freedom.
Calculation of the rescaled variance test for I(0) against long-memory alternatives. The statistic is the centered kpss statistic based on the deviation from the mean. The limit distribution of this statistic is a Brownian bridge whose distribution is related to the distribution of the Kolmogorov s