Software Listing: Multivariate
- Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck
- License: Freeware
- Price: 0.00

To walk through the code and for a thorough description, refer to A. Meucci (2009) , "Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck", Latest version of article and code available at http://symmys.com/node/132.
- Publisher: Attilio Meucci
- Date: 26-04-2013
- Size: 143 KB
- Platform: Matlab, Scripts
- Multivariate Nonlinear optimization using Marquardt Method
- License: Freeware
- Price: 0.00

We use the power of symbolic toolbox in order to find the optimal point in an arbitrary multivariate function Change the following code based on your objective function and run the program Function F in Symbolic Format a='(x1^2+x2-11)^2+(x1+x2^2-7)^2'; The code will iterate and converge to x and y of the optimal point.
- Publisher: Siamak Faridani
- Date: 07-06-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- 2D Multivariate Gaussian
- License: Shareware

this function plots the 2D multivariate gaussian when the mean and covariance are provided. It does not use for loops. ex: plot mean=[10;11],cov=[6 0;0 6] 2D multivariate gaussian function >> mvg([10;11],[6 0;0 6]).
- Publisher: Chathurika Dharmagunawaradhana
- Date: 02-04-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Visual Stats
- License: Shareware
- Price: 120

Implement data analysis and multivariate statistical analysis. 1. Probability analysis. 2. Compute descriptive statistics of selected data - compute probability density function values, cumulative density function values, quantile values, means and variances. 3. Frequency analysis. 4. Compare means- one sample t test, independent-samples t test and paired-samples t test. 5. Compare variances. 6. Variance analysis - one-way ANOVA and two-way ANOVA. 7. Univariate linear regression and multivariate linear regression. 8. Linear curve fitting and nonlinear curve fitting. 9. Excel-like data editor is easy to use.
- Publisher: GraphNow
- Date: 01-12-2008
- Size: 2216 KB
- Platform: Win2000, Windows Vista, WinOther
- The Unscrambler
- License: Demo
- Price: 0.00

The Unscrambler® is the complete multivariate analysis and experimental design software, equipped with powerful methods including Principal Component Analysis (PCA), Multivariate Curve Resolution (MCR), Partial Least Squares Regression (PLS-R), 3-Way PLS Regression, K-Means Clustering and SIMCA Classification.
Extensively used across a wide range of research and industrial applications including advanced Chemometrics, Spectroscopy and cutting-edge Sensometrics, this market-leading software yields demand-driven formulations, process optimization, cost- savings and increased ROI in product development, process control, quality control and R&D.
- Publisher: CAMO
- Date: 03-06-2011
- Size: 31755 KB
- Platform: Win2000, Windows Server, WinOther
- Multivariate Split Test Optimizer
- License: Shareware

Easy to use php tool for multivariate split testing. Integrates with any php cart software. Easy to understand stats, reports statistically significant data so you know if you're making the right choice about which version is best. Self hosted, fast, secure. No javascript, no 3rd party involved. Features: keep yourself out of the results ip blockingmultiple steps ... can track more than one step... e.g. product page > to cart > to checkout > to thanks pagecan log sale and price datacan modify a test while its running... notice that one element of your test is 99% confident that it sucks... you can turn those elements off without effecting the testyou can weight the default view so it gets showed more often.
- Publisher: valuephp
- Date: 05-02-2011
- Platform: PHP, Scripts
- RiskMetrics
- License: Freeware
- Price: 0.00

RiskMetrics.m: Estimates the univariate or multivariate RiskMetrics. USAGE: rm = RiskMetrics(data,alpha) INPUTS: data = ( m x n ) vector lamba = the scale parameter method = Univariate or Multivariate OUTPUTS: rm = ( m x n ) volatility vector for the univariate case or an [( n x n )x m] covariance vector for the multivariate case Please feel free to contact the author at a.gabrielsen@city.ac.uk with comments, suggestions, or bugfixes..
- Publisher: Alexandros Gabrielsen
- Date: 13-02-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Multivariate Gaussian Mixture Model Optimization by Cross Entropy
- License: Freeware
- Price: 0.00

Fit a multivariate gaussian mixture by a cross-entropy method. Cross-Entropy is a powerfull tool to achieve stochastic multi-extremum optimization. Please visit http://iew3.technion.ac.il/CE/ for more informations i) Please compile mex-files by the mexme_ce_gauss.m (if compiler is not setup, run mex -setup before. ii) Run the program demo test_ce_mvgm.m.
- Publisher: Sebastien Paris
- Date: 04-01-2013
- Size: 31 KB
- Platform: Matlab, Scripts
- Free Split and Merge Expectation Maximization for MultiVariate Gaussian Mixture
- License: Freeware
- Price: 0.00

Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds Usage ------- [logl , M , S , P] = fsmem_mvgm(Z , [M0] , [S0] , [P0] , [option]); Inputs ------- Z Measurements (d x N) M0 Initial mean vector. M0 can be (d x 1 x K) (default [Kini random elements from Z]) S0 Initial covariance matrix. S0 can be (d x d x K) (default [cov(Z)/40]) P0 Initial mixture probablities (1 x 1 x K) : (default [1/Kini]) options Kini Initial number of compounds (default [5]) Kmax Maximum number of compounds (default [15]) maxite_fsmem Number of maximum iteration for the main loop of the fsmem (default [100]) maxite_fullem Number of maximum iteration for the full EM inside the main loop (default [100]) maxite_partialem Number...
- Publisher: Sebastien Paris
- Date: 10-04-2013
- Size: 215 KB
- Platform: Matlab, Scripts
- First-order multivariate calibration
- License: Freeware
- Price: 0.00

Graphical user interface driven first-order multivariate calibration using up-to-date methods, including PCR, PLS, net analyte methods and ortoghonal signal correction. Provides a variety of pre-processing tools, cross-validation plots, prediction plots, elliptical joint region plots, net analyte signal regression plots, and additional information on statistical analysis of results. Includes a user manual and example data..
- Publisher: Alejandro Olivieri
- Date: 15-03-2013
- Size: 532 KB
- Platform: Matlab, Scripts
- EM for HMM Multivariate Gaussian processes
- License: Shareware

em_ghmm : Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement Usage ------- [logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options]); Inputs ------- Z Measurements (m x K x n1 x ... x nl) PI0 Initial probabilities (d x 1) : Pr(x_1 = i) , i=1,...,d. PI0 can be (d x 1 x v1 x ... x vr) A0 Initial state transition probabilities matrix Pr(x_{k} = i| x_{k - 1} = j) such sum_{x_k}(A0) = 1 => sum(A , 1) = 1. A0 can be (d x d x v1 x ... x vr). M0 Initial mean vector. M0 can be (m x 1 x d x v1 x ... x vr) S0 Initial covariance matrix. S0 can be (m x m x d x v1 x .
- Publisher: Sebastien Paris
- Date: 25-03-2013
- Size: 20 KB
- Platform: Matlab, Scripts
- Multivariate analysis and preprocessing of spectral data
- License: Shareware

SPECTRAL_MVA is a GUI for running Multivariate analysis of spectroscopic data Initially designed for analysis of X-ray Photoelectron spectra, can be used for analysis of any type of data tables, containing spectra or any other data Opens MAT files with or without a variable X. Opens VMS files (XPS spectra) either from original vision software or CASAXPS software Preprocessing options of SMOOTHING, NORMALIZING, DERIVATIZING and SHIFTING spectra Three MVA methods - PCA, SIMPLISMA and MCR PLS_TOOLBOX from Eigenvector is a must.
- Publisher: Kateryna Artyushkova
- Date: 17-03-2013
- Size: 51 KB
- Platform: Matlab, Scripts
- Multivariate Lognormal Simulation with Correlation
- License: Shareware

MVLOGNRAND MultiVariate Lognormal random numbers with correlation. This function will generate multivariate lognormal random numbers with correlation. Often one would simulation a lognormal distribution by first simulating a normal and then taking the exponent of it. If you provide the correlation matrix to the multivariate normal random number generator and then exponeniate the results, you will not have the correlation stucture you input in the normal distribution because of the exponeniation. This function adjusts for that and passes the adjusted correlation matrix to the normal random number generator.
- Publisher: Stephen Lienhard
- Date: 08-02-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Simulations with Exact Means and Covariances
- License: Freeware
- Price: 0.00

Multivariate normal simulations where sample mean and covariances match the respective population moments, refer to A. Meucci (2009), "Simulations with Exact Means and Covariances", Latest version of article and code available at http://symmys.com/node/162.
- Publisher: Attilio Meucci
- Date: 21-04-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- GUI for Multivariate Image Analysis of 4-dimensional data
- License: Shareware

This GUI includes is a set of multivariate image analysis methods for analyzing image data sets acquired at two variables. For example: emission excitation image data, spectral or dynamic (temporal) sequences of images acquired at different depths using microscopy. Two approaches are included: 1. 2-step two-way MIA using PCA, MCR. MAF and Simplisma. In this method, image sequences as a function of variable1 at fixed variable2 are analyzed by two-way method during the 1st step and then the resulted score images at each variable 2 are combined into a new data set and are analyzed by the same two-way method at the 2nd step.
- Publisher: Kateryna Artyushkova
- Date: 14-06-2013
- Size: 594 KB
- Platform: Matlab, Scripts
- MVG Multivariate Gaussian random number generator
- License: Shareware

MVG is a multivariate Gaussian (normal) random number generator. A user can generate a vector from the multivariate normal distribution of any dimension by specifying a mean vector and symmetric positive-definite covariance matrix. A linear transformation based on the Cholesky decomposition of the covariance matrix is applied to a set of realizations from the distribution N(0,I). By applying the linear transformation to those samples, the output is a matrix whose columns are samples drawn from the distribution N(mu,Sigma) where mu is the specified mean vector and Sigma is an SPD covariance matrix.
- Publisher: Chad Lieberman
- Date: 02-03-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- HotellingT2
- License: Shareware

Hotelling's T-Squared multivariate test for one sample, two independent samples [homoskedasticity or heteroskedasticity (to test)] and two dependent samples..
- Publisher: Antonio Trujillo-Ortiz
- Date: 22-04-2013
- Size: 72 KB
- Platform: Matlab, Scripts
- Multivariate normal random vectors with fixed mean and covariance matrix
- License: Shareware

MVNRND2 Random vectors from the multivariate normal distribution. R = MVNRND2(MU,SIGMA,NUM) returns a NUM-by-D matrix R of multivariate normal random vectors whose mean and covariance matrix match the given input parameters, MU (1-D vector) and SIGMA (D-by-D matrix) [...] = MVNRND2(...,COVNORM) determines normalization for covariance 0 : Normalizes by NUM-1. This makes cov(R) the best unbiased estimate of the covariance matrix (Default) 1 : Normalizes by NUM and produces the second moment matrix of the observations about their mean. MU : Either a 1-by-D row vector, or a scalar across dimensions.
- Publisher: Mike Sheppard
- Date: 05-03-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Total Kullback-Leibler (tKL) divergence between multivariate normal probability density functions
- License: Shareware

tKL between two multivariate normal probability density functions. This program implements the tKL between two multivariate normal probability density functions following the references: Baba C. Vemuri, Meizhu Liu, Shun-Ichi Amari and Frank Nielsen, Total Bregman Divergence and its Applications to DTI Analysis, IEEE Transactions on Medical Imaging (TMI'10), 2010. Meizhu Liu, Baba C. Vemuri, Shun-Ichi Amari and Frank Nielsen, Total Bregman Divergence and its Applications to Shape Retrieval, IEEE Conference on Computer Vision and Pattern Recognition (CVPR'10), 2010. If you use this code in any form, please cite the above papers.
- Publisher: Meizhu Liu
- Date: 23-03-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Batch Multivariate Bandwidth Estimator for KDE
- License: Shareware

Batch multivariate kernel density estimator from weighted data. The submission includes a code for estimating a multivariate bandwidth ("getBandwidth.m") matrix for a Gaussian Kernel Density Estimator. The included demonstration code ("demoBWEstimation.m") estimates a bandwidth from a weighted set of data-points and displays the resulting KDE by tabulating it, as well as displaying it as a Gaussian Mixture Model. The bandwidth calculation is a special case of a more general bandwidth estimator [1], which was developed originally for online density estimation. [1] Kristan et al., Multivariate Online Kernel Density Estimation with Gaussian Kernels, Pattern Recognition, 2011 (url: http://vicos.
- Publisher: Matej Kristan
- Date: 06-05-2013
- Size: 20 KB
- Platform: Matlab, Scripts










