Quantile regression

Quantile regression

Quantile regression with LP or interior method.It has kernel test and wald test.See example in readme.m

Systemic Risk

A framework for systemic risk valuation and analysis.

Bivariate kernel density and regression

Bivariate kernel density, kernel regression, and kernel quantile regression

Returns, for two data series:Marginal kernel densitiesBivariate kernel densityConditional kernel densityNadaraya-Watson kernel regressionkernel quantile regressionMethod: Gaussian kernel, Silverman

MIDAS Matlab Toolbox

Repack of Mi(xed) Da(ta) S(ampling) regressions (MIDAS) written by Eric Ghysels and collaborators

The mixed frequency regression studies the explanatory power of high frequency variables on the low frequency outcome. The weights associated with high frequency regressors are usually assumed some

Non-crossing polynomial quantile regression

Non-crossing polynomial quantile regression

ncquantreg finds the coefficients of a polynomial p(x) of degree n that fits the data in vector x to the quantiles tau of y.ncquantreg(x,y) performs median regression (tau = 0.5) using a polynomial

quantreg(x,y,tau,order,Nboot)

Quantile regression with bootstrapping confidence intervals

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

Variational Bayesian Monte Carlo (VBMC): Bayesian inference

Variational Bayesian Monte Carlo (VBMC) algorithm for Bayesian posterior and model inference in MATLAB

MATLAB for R Users in Computational Finance

Learn how to use MATLAB and R together to tackle your computational needs

Quantile-quantile plot

Quantile-quantile plot with patch option

NOTE: this function is now available from the IoSR Matlab Toolbox as iosr.statistics.qqPlot. ------------------------- qq_plot(y) displays a quantile-quantile plot of the sample quantiles of y versus

Quantile Probability Plot

Quantile Probability Plot

This code generates Quantile Probability Plots, often used in investigating the distribution of reaction times when there are several conditions and several subjects. With this code you can easily

Multivariate Polynomial Regression

Performs polynomial regression on multidimensional data.

Performs Multivariate Polynomial Regression on multidimensional data. The fits are limited to standard polynomial bases with minor modification options. Feel free to implement a term reduction

Quantile calculation

Quantiles of a sample via various methods

NOTE: this function is now available from the IoSR Matlab Toolbox as iosr.statistics.quantile. ------------------------- This function calculates quantiles and weighted quantiles for vectors

Normal Quantile with Precision

computes the normal quantile function with high precision for extreme values in the tail

computes the quantile function of the standard normal distribution, truncated to the interval [l,u].Method designed for precision in the tails. Inf values for vectors 'l' and 'u' accepted;%Example

quantile_on_quantile

This is an implementation of Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of

# Quantile-on-Quantile Regression (QQR) Toolbox for MATLABA MATLAB implementation of the Quantile-on-Quantile Regression method for analyzing dependence structures across conditional distributions

Inference on quantiles: confidence intervals, p-values, and testing

Improved quantile inference for one- and two-sample (e.g., treatment vs. control) cases

Detailed documentation includes further explanation and examples; just type "help quantile_inf". The following briefly describes functionality as well as the theoretical foundations from the

sippi

A Matlab toolbox for sampling inverse problems with complex prior information.

ISO 226:2003 Normal equal-loudness-level contours

Return sound pressure levels of pure tone frequencies at specified loudness level(s).

Impulse response acoustic information calculator

Calculate RT, DRR, Cte, and EDT for impulse response file

Sinc filter

Apply a near-ideal low- or band-pass filter.

Gammatone filterbank

Produce an array of responses from a fourth-order Gammatone filter via FFT

Support Vector Regression

On-line support vector regression (using Gaussian kernel)

On-line regression On-line learning algorithms are not restricted to classification problems. The update rule for the kernel adatron algorithm also suggests a general methodology for creating on-line

STK

The STK is a (not so) Small Toolbox for Kriging

Experiments(DACE), the STK can be useful for other applicationsareas (such as Geostatistics, Machine Learning,Non-parametric Regression, etc.).Copyright: Large portions are Copyright (C) 2011-2014 SUPELECand

Orthogonal Linear Regression

Fit data using orthogonal linear regression.

each datapoint DATA(i,:) -- LINORTFITN finds N and C such that the sum of squared distances is minimized.There is already a file in Matlab Central for orthogonal linear regression in 2 dimensions, but it

Nonlinear Regression using ANFIS in MATLAB

Application of ANFIS to multi-variable nonlinear regression, function approximation and modleing

For more information, see following links:http://yarpiz.com/301/ypfz101-nonlinear-regression-using-anfis

Regression Utilities

A variety of regression utilities

This zip file contains 11 functions related to regression. The functions are:1) cookdist.m - Cook's distance for data points2) dregr.m - Deming regression3) irsvdregr.m - Iterative Reweighted Least

FOMCON Toolbox for MATLAB

FOMCON toolbox for MATLAB is dedicated to fractional-order modeling and control of dynamic systems.

Linear Regression [Simplest Implementation]

Linear regression using: Direct Method, Inbuilt function, SGD Method

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is

Alternative box plot

Draw a box plot with various display options

Automated construction of a legend. - Set box limits as percentiles. - Set whisker extent via various methods.- Use of weighted quantiles.- Creation of violin plots.

Student's t regression

Estimates a Student's t regression model

Estimates a Student's t regression model:y = X*beta + epswhere eps ~ Student's t (0, sigma, nu).with nu > 2.Parameters are estimated with maximum likelihood.

Kernel Smoothing Regression

A non-parametrical regression (smoothing) tool using Gaussian kernel.

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

Boosted Binary Regression Trees

Boosted Binary Regression Trees is a powerful regression method which can handle vector targets.

Boosted Binary Regression Trees (BBRT) is a powerful regression method proposed in [1]. BBRT combines binary regression trees [3] using a gradient boosting technique.There are several variants

GP regression

Gaussian Process regression

Gaussian Process regression based on posterior updates of noise-less observations

Five parameters logistic regression - There and back again

Fit data points with a five points logistic regression or interpolate data.

Five parameters logistic regressionOne big holes into MatLab cftool function is the absence of Logistic Functions. In particular, The Five Parameters Logistic Regression or 5PL nonlinear regression

Differential Evolution

Optimization using the evolutionary algorithm of Differential Evolution.

Binned Scatter Plot

plot quantiles of y given quantiles of x.

inter-quartile range. X and Y must have the same number of rows or columns.This is useful to visualize two-dimensional distributions.Options include e.g.: plotting different quantiles; means/variances instead of

Regression Outliers

Removes outliers from X and Y variables based on regression residuals

This function accepts two (vector of) variables for which a bivariate linear regression analysis is meant to be performed, and removes the outliers from both variables. Since the regression residual

Four parameters logistic regression - There and back again

Fit data points with a four points logistic regression or interpolate data.

Four parameters logistic regression.One big holes into MatLab cftool function is the absence of Logistic Functions. In particular, The Four Parameters Logistic Regression or 4PL nonlinear regression

Long term average spectrum

Calculate the long-term average spectrum of a signal

Linear Regression plot with Confidence Intervals in MATLAB

Sample code to plot linear regression curve with confidence intervals.

This is a simplified code to generate a linear regression curve for your paper/report/assignment. Just replace the sample data and comment the line 17 : axis([0.04 0.3 0.03 .35]);This code is

Linear Regression with Errors in X and Y

Calculates slope and intercept for linear regression of data with errors in X and Y.

Calculates slope and intercept for linear regression of data with errors in X and Y. The errors can be specified as varying point to point, as can the correlation of the errors in X and Y.The

Passing and Bablok regression

Passing & Bablok regression is a linear regression procedure usefull for comparing clinical methods

. Classical linear regression method assume that variables X and Y are normal distributed and with a measurement error costant over the range of concentrations.However, in method comparison studies we generally

Probabilistic Linear Regression

Probabilistic Linear Regression with automatic model selection

This package contains functions that fit a probabilistic linear regression model. For the ordinary regularized linear regression, user has to manually assign the regularization parameter. However

LOESS regression smoothing

LOESS performs a locally weighted regression fit to noisy data

Function fLOESS performs LOESS (locally weighted non-parametric regression fitting using a 2nd order polynomial) smoothing to one dimensional data, without the Matlab Curve Fitting Toolbox. This

Gaussian Process Regression (GPR)

Gaussian Process Regression using GPML toolbox V4.2

Kernel Ridge Regression

This is ridge regression implemented using the Gaussian Kernel.

The Gaussian Kernel can be changed to any desired kernel. However such a change will not dramatically improve results. This is a variant of ridge regression using the kernel trick (Mercers Theorem).

Interactive regression on a plot

Perform regression on plotted data in a figure by manually choosing the regression area.

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

Logistic Regression

Multiclass Logistic Regression Classification

K class Logistic Regression Classification based on K binary logistic classifiers

Bayesian Linear Regression

This is a set of MATLAB functions to do Bayesian linear regression

This is a set of MATLAB functions to do Bayesian linear regression. Derivations are also included.

mGstat

Some tools for geostatistical analysis

Brainstorm

Brainstorm: Open source application for MEG/EEG data analysis

Support Vector Regression (SVR)

Support Vector Regression (SVR)

Support Vector Regression (SVR)Support Vector Regression (SVR) is a type of regression analysis that uses Support Vector Machines (SVMs) to perform linear or nonlinear regression. Similar to SVMs for

MNI2FS: High Resolution Surface Rendering of MNI Registered Volumes

MNI2FS: High Resolution Surface Rendering of MNI Space Volumes

Local Regression 2D

Local Regression for 2D Data with plot/figure

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

Theil-Sen Robust Linear Regression

Quickly perform linear regression that is robust to outliers.

This function executes a fast version of the non-parametric Theil-Sen robust linear regression algorithm by finding the median slope between all pairwise combinations of points in a given data

MATDRAM: Delayed-Rejection Adaptive Metropolis MCMC

MatDRAM is a pure-MATLAB Adaptive Markov Chain Monte Carlo simulation and visualization library.

, sampling, and integration of mathematical objective functions of arbitrary-dimensions, in particular, the posterior probability distributions of Bayesian regression models in data science, Machine Learning

Regression with Gradient Descent

Regression with Gradient Descent; A coefficient finding technique for the desired system model.

Regression with Gradient Descent; A coefficient finding technique for the desired system modelI included different functions to model the data using descent gradient techniqueperformed Linear

Local Linear Kernel Regression

A function to provide local linear estimator of Gaussian kernel regression

This is the local linear version of the kernel smoothing regression function: http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=19195&objectType=FILEThe local linear

hctsa

Highly comparative time-series analysis

Nonlinear Regression Shapes

Curve fitting, empirical modeling, and an appreciation of shape

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

Linear Deming Regression

deming perfoms a linear Deming regression. Useful when errors are present in both x and y variables.

[ b sigma2_x x_est y_est stats] = deming(x,y,lambda,alpha)deming() performs a linear Deming regression to find the linear coefficients: y = b(1) + b(2)*xunder the assumptions