select calculates semiparametric estimates of the intercept and slope coefficients in the "outcome" or "level" equation of a self-selection model. It is the second step of the two-step estimator of these models. It combines the procedures in the quantlets powell (slope estimator) and andrews (inter
Controls the layout of a display. First, the display should be created and shown. Then call setgopt to change its headline, the labels of its axes, limits, etc.
settime generates from a set of vectors a vector of time points. The parameter yr (year) is necessary, the parameters mo (month), dy (day of month), hr (hour), mn (minute), sc (second) are optional. The granulation of the measurement can be changed with the command settimegran. Each value in t
SIMEX (SIMulation EXtrapolation) is a simulation-based method of estimating and reducing bias due to measurement error. simex is applicable to general estimation methods, for example, least-squares, maximum likelihood, quasi-likelihood, etc.
Simulation of discrete observations of a Geometric Brownian Motion (GBM) via direct integration (method=1) or Euler scheme (method=2). The process follows the stochastic differential equation: dX(t) = mu X(t) dt + sigma X(t) dW(t).
Simulation of discrete observations of a generalized Ornstein-Uhlenbeck process via Euler scheme. The process follows the stochastic differential equation: dX(t) = beta (L - X(t)) dt + sigma (X(t)^gamma) dW(t).
generates real-life trajectory of the risk process from given data with premium corresponding to the non-homogeneous Poisson process and incorporating emprirical mean loss.
plots real-life trajectory of the risk process from given data with the premium corresponding to non-homogeneous Poisson process and incorporating given mean loss value.
Simulation of discrete observations of an Ornstein-Uhlenbeck process via its transition probability law. The simulated process follows the stochastic differential equation: dX(t) = aX(t) dt + s dW(t).
sknn computes the k-nearest neighbour smooth regression from scatter plot data. As inputs you have to specify the explanatory variable x, the dependent variable y and the smoothing parameter k.
Softthresholds the mother wavelet coefficients b1 and b2 interactively. The user is a threshold offered by sqrt(2 sigma n). To compute the threshold value only b1 and x is used.
sort sorts the rows of a matrix. If column c1 is specified the matrix will be sorted with respect to column c1. That is, the rows of the matrix will be arranged in order that elements of column c1 are in ascending (descending) order.
Computes the Average Delays of a one-sided CUSUM chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
Computes the Average Run Lengths of a one-sided CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the one-sided CUSUM chart run length for given i. The data are normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i))and the cumulated distribution function (CDF, P(L<=i)) of the one-sided CUSUM chart run length up to a given i. The data are normally distributed with variance 1.
Computes the Average Delays of a two-sided CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
Computes the Average Run Lengths of a two-sided CUSUM chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
Computes the probability mass function (PMF, (P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided CUSUM chart run length for given i. The data are normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided CUSUM chart run length up to a given i. The data are normally distributed with variance 1.
Computes the Average Delays of the Crosier-CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
Computes the Average Run Lengths of the Crosier-CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the Crosier CUSUM chart run length for given i. The data are normally distributed with variance 1.
Computes the probability mass function (PMF P(L=i)) and the cumulative distribution function (CDF P(L<=i)) of the Crosier CUSUM chart run length up to a given i. The data are normally distributed with a variance of 1.
Computes the Average Delays of a one-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
Computes the Average Run Lengths of a one-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of a one-sided EWMA chart run length for given i. The data are normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the one-sided EWMA chart run length up to a given i. The data are normally distributed with variance 1.
Computes the Average Delays of a two-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
Computes the Average Run Lengths of a two-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided EWMA chart run length for given i. The data are normally distributed with variance 1.
Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided EWMA chart run length up to a given i. The data are normally distributed with variance 1.
Uses the Breeden and Litzenberger (1978) method and a semiparametric specification of the Black-Scholes option pricing function to calculate the empirical State Price Density. The analytic formula uses an estimate of the volatility smile and its first and second derivative to calculate the State-pr
Using the assumptions of the Black-Scholes call-option pricing formula this quantlet calculates the Black- Scholes State-Price Density, Delta and Gamma from call-options data
computes spatial correlograms of spatial data or residuals. Initially, it divides the range of the data into nint bins, computes the covariance for pairs with separation in each bin, then divides by the variance. It returns results only for bins with 6 or more pairs.
computes spatial (semi-)variograms of spatial data or residuals. Initially, it divides the range of the data into nint bins, and computes the average squared difference for pairs with separation in each bin. It returns results only for bins with 6 or more pairs.
simulates a Binomial (Poisson) spatial point process. Note that SPPPinit or SPPPsetregion must have been called before to set the domain. To be able to reproduce results, reset the random number generator for point processes by calling SPPPinitrandom first.
simulates a SSI (sequential spatial inhibition) point process. Note that SPPPinit or SPPPsetregion must have been called before to set the domain. To be able to reproduce results, reset the random number generator for point processes by calling SPPPinitrandom first. Note that this quantlet will not
simulates a Strauss spatial point process. It uses a spatial birth-and-death process for (4 n) steps (or for (40 n) steps when starting from a binomial pattern on the first call from another function). Note that SPPPinit or SPPPsetregion must have been called before to set the domain. To be able to
Additive component analysis in additive separable models using wavelet estimation. An additive component can be tested against a given polynomial form with degree p, e.g. when p is set to zero we test for significant influence of that component. The procedure is presented in Haerdle, Sperlich,
Stein computes the optimal threshold for a vector of data plus noise so that the mean squared error is minimized. Stein uses Stein's unbiased risk estimator for the risk. The quantlet sure uses stein to threshold the father and mother wavelet coefficients.
estimates for a given dataset of a random process the parameters of the following two models: a Wiener Process (model 1) and a compounded Poisson Jump Process mixed with a Wiener Process (model 2)
using the given data stockestsim estimates the parameters for the following models: model 1, a Wiener Process, and model 2, a Wiener Process with jumps which are following a compounded Poisson Jump Process; after that both models are compared with the real dataset by a simulation.
simulates random processes for a stock price by three different ways: 1. using a Wiener Process, 2. using a compounded Poisson Jump Process with a log normal distribution of jump height and 3. using a mixture of both.
Auxiliary routine for rICfil: calculates for dimension p>(=)2 diag(E[ YY' u min(b/|aIhY|,u) ]) and diag(E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_p-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{
Auxiliary routine for rICfil: calculates for dimension p>(=)2 (E[ YY' u min(b/|aIhY|,u) ]) and (E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_p-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{-1/2} La
splits a string into tokens. Therefore a set of delimiters has to be specified, i.e., characters that separate the tokens, which are not returned themselves.
provides a short summary table (min, max, mean, median standard error) for all columns of a data matrix. An additional vector of name strings can be given to identify columns by names.
provides a summary table (containing: N, Nmiss, min, max, mean, standard error, 1%, 10%, 25%, 50%, 75%, 90%, 99% quantiles) for all columns of a data matrix. Missings values are omitted. An additional vector of name strings can be given to identify the columns by names.
Sure denoises wavelet coefficients so that the mean squared error is minimized. MSE is estimated by Stein's unbiased risk estimator based on the variance of the coefficients. Sure computes the optimal threshold for the father wavelets and each level of mother wavelets. The input arrays can be obtai
Sure denoises wavelet coefficients. If the stein procedure is chosen, the mean squared error is minimized. MSE is estimated by Stein's unbiased risk estimator based on the variance of the coefficients. Sure computes then the optimal threshold for the father wavelets and each level of mother wavelet
computes the singular value decomposition of an n x p matrix x (n >= p). The singular value decomposition finds matrices u, l, v such that x = u*l*v', where u and v are orthogonal matrices and l is a diagonal matrix.
returns the vector of scores for the objects represented in AC. AT is a training set where the last column describes the class of an object (must be +1 or -1).
switch allows the selection of one or more alternatives of many. Each alternative is introduced by a case statement. Similar to if-endif it controls, whether the following block is processed or not. The keyword break serves as end marker of case and leaves the switch block at the position of endsw.
Calculates the symmetric root of a symmetric positive semidefinite matrix,(s.p.s.d.) ie. symroot(x)=symroot(x)' and symroot(x)*symroot(x) = x. uesful for simulation of multivariate normal variates with a given Covariance Structure