Fuzzy Theil regression Model

Theil-Sen Estimators in a Multiple Linear Regression Model

In this article, we propose the Theil-Sen estimators of parameters in a multiple linear regression model based on a multivariate median, generalizing the Theil-Sen estimator in a simple linear regression model. The proposed estimator is shown to be robust, consistent and asymptotically normal under mild conditions, and super-efficient when the error distribution is discontinuous. It can be chos...

A SAS Macro for Theil Regression

Deming, Theil-Sen, and Passing-Bablock Regression

Least squares regression of y on x assumes that the x variate is measured without error, and minimizes the sum of squared vertical distance between the data points y and the fitted regression line. Regression of x on y minimizes the horizontal distances. Adcock [1] in 1878 suggested minimizing the sum of squared horizontal + vertical distances to the predicted values. However the idea of Adcock...

Multiple Fuzzy Regression Model for Fuzzy Input-Output Data

A novel approach to the problem of regression modeling for fuzzy input-output data is introduced.In order to estimate the parameters of the model, a distance on the space of interval-valued quantities is employed.By minimizing the sum of squared errors, a class of regression models is derived based on the interval-valued data obtained from the $alpha$-level sets of fuzzy input-output data.Then,...

Estimation of Bivariate Regression Data via Theil ’ S Algorithm

Ekezie Dan Dan and Opara, Jude Department of Statistics, Imo State University PMB 2000, Owerri, Nigeria. Corresponding Author: Ekezie Dan Dan -----------------------------------------------------------------------------------------------------------------------Abstract This paper is on the estimation of bivariate regression data using Theil’s algorithm. This method was adopted since all errors ...