Linear trimmed means for the linear regression with AR(1) errors model

نویسندگان

  • Yi-Hsuan Lai
  • Lin-An Chen
چکیده

For the linear regression with AR(1) errors model, the robust generalized and feasible generalized estimators of Lai et al. (2003) of regression parameters are shown to have the desired property of a robust Gauss Markov theorem. This is done by showing that these two estimators are the best among classes of linear trimmed means. Monte Carlo and data analysis for this technique have been performed. & 2010 Elsevier B.V. All rights reserved.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Diagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions

Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are ...

متن کامل

Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors

In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcom...

متن کامل

ESTIMATING THE PARAMETERS OF A FUZZY LINEAR REGRESSION MODEL

Fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. Several methods for evaluating fuzzy coefficients in linear regression models have been proposed. The first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. In this the...

متن کامل

A WEIGHTED LINEAR REGRESSION MODEL FOR IMPERCISE RESPONSE

A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.

متن کامل

Orthogonal Least Trimmed Absolute Deviation Estimator for Multiple Linear Errors-in-Variables Model

Orthogonal least trimmed absolute deviation (OLTAD) estimator of the multiple linear errors-in-variables (EIV) model is presented. We show that the OLTAD estimator has the high breakdown point and appropriate properties. A new decimal-integer-coded genetic algorithm(DICGA) and Fast-OLTAD method for solving OLTAD estimators are also proposed. Computational experiments of the OLTAD estimator of t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010