A simulation study of bias in estimation of variance by bootstrap linear regression model

1988 ◽  
Vol 17 (3) ◽  
pp. 871-886
Author(s):  
Baha M. D. Alkuzweny ◽  
Donald A. Anderson
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Estimation Of Variance

OPTIMAL SIMILAR TESTS FOR STRUCTURAL CHANGE FOR THE LINEAR REGRESSION MODEL

2002 ◽  
Vol 18 (4) ◽  
pp. 853-867 ◽  
Author(s):  
G. Forchini
Keyword(s):  
Structural Change ◽  
Linear Regression ◽  
Regression Model ◽  
Simulation Study ◽  
Linear Regression Model ◽  
American Mathematical Society ◽  
Error Variance ◽  
Optimal Test ◽  
Finite Samples ◽  
Power Comparisons

This paper analyzes similar tests for structural change for the normal linear regression model in finite samples. Using the approach of Wald (1943, American Mathematical Society Transactions 54, 426–482), Hillier (1987, Econometric Theory 3, 1–44), Andrews and Ploberger (1994, Econometrica 62, 1382–1414), and Andrews, Lee, and Ploberger (1996, Journal of Econometrics 70, 9–36), we characterize a class of optimal similar tests for the existence of (possibly multiple) changepoints at unknown times. We extend the analysis of Andrews et al. (1996) by deriving weighted optimal similar tests for the case where the error variance is not known. We also show that when the sample size is large, the tests of Andrews et al. constructed by replacing the error variance with an estimate are equivalent to the optimal test derived in this paper. Power comparisons are provided by a small simulation study.

scholarly journals POWER AND SIZE OF NORMAL DISTRIBUTION AND ITS APPLICATIONS

2017 ◽  
Vol 9 (2) ◽  
pp. 111
Author(s):  
Budi Pratikno ◽  
Jajang Jajang ◽  
Setianingsih Setianingsih ◽  
Raden Sudarwo
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Regression Model ◽  
Normal Distribution ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Graphical Analysis ◽  
Maximum Power ◽  
Minimum Size ◽  
Large Standard Deviation

. The research studied power and size of normal distribution and its applications on linear regression model. The power and size formulas are derived, and the unrestricted test (UT), restrcited test (RT) and pre-test test (PTT) are used. The recommendation of the test is given by choosing maximum power and minimum size, and also graphical analysis. The result showed that the power and size for large standard deviation () tend to be identical and flat. In simulation study, the graphs of the UT, RT, and PTT are still similar to the previous research (Pratikno, 2012), where the PTT  tend to lie between UT and RT.

scholarly journals The Performance of Some Restricted Estimators In Restricted Linear Regression Model

2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Bader Aboud ◽  
Mustafa Ismaeel Naif
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Regression Estimator ◽  
Mean Square ◽  
Ridge Regression Estimator ◽  
The Mean ◽  
Biased Estimators ◽  
Better Than

In the linear regression model, the restricted biased estimation as one of important  methods to addressing the high variance and the  multicollinearity problems. In this paper, we make the simulation study of the some restricted biased estimators. The mean square error (MME) criteria are used to make a comparison  among them. According to the simulation study we observe that, the performance of the restricted modified unbiased  ridge regression estimator (RMUR) was proposed by  Bader and Alheety (2020)  is better than  of these estimators. Numerical example have been considered to illustrate the performance of the estimators.

scholarly journals Distributional consistency of the lasso by perturbation bootstrap

Biometrika ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 957-964
Author(s):  
Debraj Das ◽  
S N Lahiri
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Bootstrap Method ◽  
Estimation Procedure ◽  
Finite Sample ◽  
Finite Sample Properties ◽  
Lasso Estimator

Summary The lasso is a popular estimation procedure in multiple linear regression. We develop and establish the validity of a perturbation bootstrap method for approximating the distribution of the lasso estimator in a heteroscedastic linear regression model. We allow the underlying covariates to be either random or nonrandom, and show that the proposed bootstrap method works irrespective of the nature of the covariates. We also investigate finite-sample properties of the proposed bootstrap method in a moderately large simulation study.

scholarly journals Performance of Some Stochastic Restricted Ridge Estimator in Linear Regression Model

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jibo Wu ◽  
Chaolin Liu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Ridge Regression ◽  
Ridge Estimator ◽  
Numerical Example ◽  
Regression Estimators ◽  
Mixed Estimator

This paper considers several estimators for estimating the stochastic restricted ridge regression estimators. A simulation study has been conducted to compare the performance of the estimators. The result from the simulation study shows that stochastic restricted ridge regression estimators outperform mixed estimator. A numerical example has been also given to illustrate the performance of the estimators.

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