Exponential Regression

Quantile Regression USAGE: [p,stats]=quantreg(x,y,tau[,order,nboot]); INPUTS: x,y: data that is fitted. (x and y should be columns) Note: that if x is a matrix with several columns then multiple linear regression is used and the "order" argument is not used. tau: quantile used in regression....

GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Expectation-Maximization (EM) iterative learning...

mex c++ interface to call Eureqa server directly from matlabThe code enables symbolic regression with possible user-defined operators (such as +, -, *, /, ^, exp, log, sin, cos, abs, tan). Applications range from a detection of hidden data relationships to nonlinear regression of different kinds...


Platforms: Matlab

The art of fitting a nonlinear regression model often starts with choosing a model form. This submission is an attempt to teach the reader a simple but general paradigm for their models as a sum of fundamental shapes that are then shifted and scaled to fit the data.I've included a bestiary of...


Platforms: Matlab

This function will perform a local regression at the given points. Using the data at hand. You will need to input a spanwidth which will tell the function how many datapoints he will use in the local regression at each evaluation.Example...


Platforms: Matlab

The purpose of this file is to provide beginners a way to understand and analyse time varying coefficient values within regression analysis particularly with financial data analysis.The file is easily customisable to suit requirements and contains information describing the code for ease.I am new...


Platforms: Matlab

This function implements multivariant Gaussian kernel regression and smoothing. A few examples show how it can be used to smooth 3-d noisy images with missing data.For tutorial purpose, the function is implemented in the simplest form. Therefore, it is particularly suitable for beginners to learn...


Platforms: Matlab

Leave-one-out cross-validation for PLS regression or discriminant analysispls_cv = plscv(x,y,vl,'da')input:x (samples x descriptors) for cross-validationy (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0.vl (1 x 1) number of latent...


Platforms: Matlab

Orthogonal Linear Regression in 3D-space by using Principal Components AnalysisThis is a wrapper function to some pieces of the code from the Statistics Toolbox demo titled "Fitting an Orthogonal Regression Using Principal Components...


Platforms: Matlab

Select a image and apply log_expT.m ti that u can get log transformed and the corresponding exponential transformed of the log transformed image. so that we can see the difference between original and exponential image.


Platforms: Matlab

ECR is a new method for regression analysis, which employs a supervising alpha to supervise the X-matrix decomposition. When alpha=0, ECR coincides with principal component regression (PCR), when alpha=1, ECR coincides with partial least squares (PLS), when alpha is between 0 and 1, ECR could...


Platforms: Matlab

Non-parametric regression is widely used in many scientific and engineering areas, such as image processing and pattern recognition.Non-parametric regression is about to estimate the conditional expectation of a random variable:E(Y|X) = f(X)where f is a non-parametric function.Based on the kernel...


Platforms: Matlab

This is the local linear version of the kernel smoothing regression function: http://www.mathworks.com/matlabcentral/fil...objectType=FILEThe local linear estimator improves the regression behaviour near the edges of the region over which the data have been collected.


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

Leave-one-out cross-validation for PLS regression or discriminant analysispls_cv = plscv(x,y,vl,'da')input:x (samples x descriptors) for cross-validationy (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0.vl (1 x 1) number of latent...


Platforms: Matlab

Kernel regression is a power full tool for smoothing, image and signal processing, etc. However, it is computationally expensive when it is extented for multivariant cases. The efficiency can be improved by only using neighbors within the effective range arond a regression point. To improve the...


Platforms: Matlab

Functions for fitting least suqares regression problems under nonnegativity constraints. Optimized version of the Lawson & Hanson approach


Platforms: Matlab

Plot data in a figure, and then interactively choose regression area. The result from polyfit is returned.Example:x=1:1:10;y=sin(x);f=figure; plot( x,y );[p,h] = figreg( f, 2 );%fit a second order polynom to selected region in plot


Platforms: Matlab

The main advantages of the Hausdorff regression are that one should not specify the direction of the errors (vertical, horizontal, orthogonal ... with the ordinary least squares) and that it is less sensitive with respect to the square of the distance.This is the original idea. Any contribution...


Platforms: Matlab

Gaussian kernel regression and local linear Gaussian kernel regression with variable window widthThis is the same as ksr and ksrlin (file File ID: #19195 and #19564), but instead of using the same bandwidth for each point, it uses a variable bandwidth given by the distance of each point to its...


Platforms: Matlab