Squared

It uses the Pierson-Moskowitz spectrum for the frequency/wave-number dependency and four different types of angular spreading like cosinus-squared, Mitsuyasu, Hasselmann and none. Requirements: - MATLAB 7 or higher Platforms: Windows, Mac, *nix, Matlab, BSD Solaris

FASTRMS Instantaneous root-mean-square (RMS) power via convolution.FASTRMS(X), when X is a vector, is the time-varying RMS power of X, computed using a 5-point rectangular window centered at each point in the signal. The output is the same size as X and contains, for each point in X, an estimate... Platforms: Matlab

Using market data in the mktdata.txt file to estimate the parameters sigma and alpha of the Constant Elasticity of Variance (CEV) model by minimizing the sum of squared differences between the model price and the market call price. Platforms: Matlab

It generates a stochastic field on a squared space. Samples are created by Latin hypercube sampling and the spatial correlation is performed by an algorithm based on Cholesky decomposition. Example inside. Platforms: Matlab

This m-file performs the conditional as well as the Chi-squared corrected for discontinuity McNemar's exact test for two dependent (correlated) samples that can occur in matched-pair studies with a dichotomous (yes-no) response. Dependent samples can also occur when the same subject is measured... Platforms: Matlab

Usage: [p, Q]= chi2test(x) The chi-squared test.Given a number of samples this function tests the hypothesis that the samples areindependent. If Q > chi2(p, nu), the hypothesis is rejected. Each column represents a variables, each row a sample. If you find any errors, please let me know: .... Platforms: Matlab

3 goodnes of fit tests for thw Weibull: Anderson-Darling, Chi-squared and graphic Platforms: Matlab

This test is based on the Wilks'method (1963) designed for detection of a single outlier from a normal multivariate sample and approaching the maximun squared Mahalanobis distance to a F distribution function by the Yang and Lee (1987) formulation. A significative squared Mahalanobis distance... Platforms: Matlab

On the literature there are available several tests of the multivariate normality (ca. 50). Among them is the graphic approach based on a chi-square quantile-quantile plot of the observations' squared Mahalanobis distances. Besides the graphic q-q approaching, in this file we proposes an... Platforms: Matlab

Hotelling's T-Squared multivariate test for one sample, two independent samples [homoskedasticity or heteroskedasticity (to test)] and two dependent samples. Platforms: Matlab

This toolbox contains a set of functions which can be used to compute the Least Median of Squares regression, the Reweighted Least Squares regression, the accociated location and scale estiamtors, and the Minimum Volume Ellipsoid. The concept is the minimization of the median of the squared... Platforms: Matlab

Statistical power of a performed (a posteriori) multivariate Hotelling's T-Squared test. File needs to input the observed multivariate F-statistic, number of data of interested sample (group) 1, number of data of complement sample (group) 2, number of variables, and significance (default =... Platforms: Matlab

Cochran's C is a set of k Chi-squared distributed sums of squares (or the corresponding variances), each of v degrees of freedom. To accept at a significance level (alpha) that the maximum variance differ from the other ones, one should examine this function. It works for equal or unequal sample... Platforms: Matlab

This code implements the 1D polynomial regression method. It uses the least square method for the finding of regression polynomial coefficents. Outputs of the script are polynomial regression coefficients, residuals, the sum of squared errors, the determination index and the graphical comparison... Platforms: Matlab

Y = UPSIDEVARIANCE(Y) returns the upside variance for columns of variable Y.Y = UPSIDEVARIANCE(Y, m) returns the upside variance using vector m as expected value for columns of variable Y.====================================================Upside variance is defined as the average sum of squared... Platforms: Matlab

Y = UPSIDECORRELATION(Y) returns the upside correlation for columns of variable Y.Y = UPSIDECORRELATION(Y, m) returns the upside correlation using vector m as expected value for columns of variable Y.====================================================Upside correlation is defined as the upside... Platforms: Matlab

Y = DOWNSIDECORRELATION(Y) returns the downside correlation for columns of variable Y.Y = DOWNSIDECORRELATION(Y, m) returns the downside correlation using vector m as expected value for columns of variable Y.====================================================Downside correlation is defined as... Platforms: Matlab

Y = DOWNSIDEVARIANCE(Y) returns the downside variance for columns of variable Y.Y = DOWNSIDEVARIANCE(Y, m) returns the downside variance using vector m as expected value for columns of variable Y.===================================================Downside variance is defined as the average sum of... Platforms: Matlab

This function estimates and plots a wavelet-based magnitude-squared (MS) coherence between two time series and both sampled at the same sampling rate.The function was designed for biological time series (EEG etc.) analysis. However, it can be applied to other stochastic time series. Platforms: Matlab

The BBL format is the main output format of the Power Graph. A BBL file describes nodes, groups of nodes (power nodes), as well as the edges between these groups of nodes (power edges). Each line contains a piece of information of the following type: * NODE {node identifier} -- Defines a single... Platforms: Mac