This is a simple function that should come with the one present on this site (Variance Ratio Test) which couldn't work without this.
Levene's F test is used to test the null hypothesis that multiple population variances corresponding to multiplesamples are equal. Prior to the analysis the data are transforming to the absolute deviation of the mean. Then perform an one-way...
Statistics fundamentals of the Correspondence Analysis (CA) is presented in the CORRAN and MCORRAN1 m-files you can find in this FEX author''s page. CA can be extended to more than two categorical variables, called Multiple Correspondence Analysis...
Generates complex generalized gaussian random variables with augmented covariance matrix Ta = [2*s 0; 0 2*s]; and shape parameter c, where c = 1 corresponds to the Gaussian case. x = cggd_rand(c,s,N) generates a vector 1xN of...
This fuction uses the "variance ratio test" and the "rkp" associated functions to compute the variance ratio ,the tests statistics and the corresponding p-values based on randomization (simulation).
This script executes some basic calculations from the following article.
Leonard AC, Franson SE, Hertzberg VS, Smith MK & Toth GP (1999) Hypothesis testing with the similarity index. Molecular Ecology 8: 2105-2114.
Please...
The script generates spatial data with a scale-invariant power spectrum (1/f noise) and a normal error distribution.
The spectral density of the data is proportional to f^BETA, where f is the frequency and BETA is the spectral exponent...
Moving variance v=movingvar(x,m) x is the timeseries. m is the window length. v is the variance. Aslak Grinsted 2005
A uniform random number generator is used to generate the binary information sequence from the binary data source. The sequence of d-de?0d-deOaos and d-de?1d-deOaos is mapped into sequence of +E and d-deOCtE where E represents the signal...
How much in variability of some variable is contributed by variance of each shock separately.
So far is for fixed number of shocks. You can change this number in the code.
Generate a Gamma random variable "Statistical Distributions", Evans, Hastings, Peacock, 2nd Edition, Wiley, 1993, p.75-81
INPUTS: (N,M) = size of array of random variables to generate b = scale...
ABSTRANS Convert spectral absorbance to transmitance TRANSABS Convert spectral transmitance to absorbance SNV Standard Normal Variate SAVGOL Savitsky-Golay smoothing and differentiation NORMALIZ Normalize matrix rows dividing...
Sample size required to achieve a specified test for difference between two variances, using the normal approximation. This estimate assumes equal or unequal sample sizes. File only needs the sample size vector, variance vector, hypothesis...
[cg, psg] = crandn(rgau,m) Generate correlated Gaussian sequences by Fourier synthesis.
Input parameters: rgau = correlation function - length n/2 m = number of realisations
Output: cg = m x n matrix...
The correlation function calculated from one realization of an ensemble is inherently flawed since the expectation operation does not come into play. Hence it is important to have an idea of the variance in the correlation function.
function [optN, C, N] = sshist(x,N) [optN, C, N] = sshist(x,N)
Function `sshist' returns the optimal number of bins in a histogram used for density estimation. Optimization principle is to minimize expected L2 loss...
>> Y = cummean(X,DIM); if X is MxN, Y is also MxN. To illustrate the functionality, lets assume X is a 1xN vector. Then, Y is a 1xN vector where the n-th entry in Y is given by mean(X(1:n)). So, Y(end) = mean(X), and Y(1) = X(1). cumvar...
this function calculates the price of Call option based on the GARCH option pricing formula of Heston and Nandi(2000). The input to the function are: current price of the underlying asset, strike price, unconditional variance of the underlying...
This set of files show some of the principles of Monte Carlo simulations, applied in the financial industry. this is the content of the web seminar called "Simulations de Monte Carlo en MATLAB".
The slides are in French and a...
Fits the Laird-Ware Linear Random Effects Model. This model assumes that for each subject y=x*b+z*g+e where x and z are known m x p and m x r matricies, b is p x 1 parameter vector and g is a r vector which has a multivariate normal distribution... |