Structural System Parameter Estimation by Two-Stage Least Squares Method

1976 ◽  
Vol 102 (5) ◽  
pp. 883-899
Author(s):  
Will Gersch ◽  
George T. Taoka ◽  
Robert Liu
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
System Parameter ◽  
Least Squares Method ◽  
Structural System ◽  
Two Stage ◽  
Two Stage Least Squares

scholarly journals About the features of the least squares method with linearization when determining nonlinear parameters of functional dependencies

2021 ◽  
Vol 2131 (2) ◽  
pp. 022132
Author(s):  
Lidia V Azarova
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Empirical Data ◽  
Least Squares Method ◽  
Functional Dependence ◽  
Functional Dependencies ◽  
Final Solution ◽  
Two Stage ◽  
Theoretical Dependence ◽  
Nonlinear Parameters

Abstract The features of approximation of empirical data by functional dependence with nonlinear parameters using the two-stage least squares method are considered in this paper. A method of simplified parameter estimation by constructing a new expression that depends on the parameters in a linear way is described. To obtain the final solution, the least squares estimation of the main dependence linearized in terms of parameters is performed. The influence of various forms of noise imposed on the theoretical dependence on the approximations is modeled.

scholarly journals A Comparison of Two-Stage Least Squares (TSLS) and Ordinary Least Squares (OLS) in Estimating the Structural Relationship between After-School Exercise and Academic Performance

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3105
Author(s):  
Kyulee Shin ◽  
Sukkyung You ◽  
Mihye Kim
Keyword(s):  
Academic Performance ◽  
Least Squares ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Structural Relationship ◽  
After School ◽  
Full Model ◽  
Two Stage ◽  
Two Stage Least Squares ◽  
Exam Scores

The current study examines the structural relationship between the academic performance exam scores of Korean middle school students and their after-school exercise hours. Although prior literature theoretically or experimentally predicts that these variables are positively associated, this association is difficult to empirically verify without controlling for mutual effects with other variables, or unless a full model is estimated by specifying the whole structure of all variables affecting the two variables in question. Unlike previous studies, this study estimates the structural relationship using two-stage least squares method, which does not require experimental observations collected for our particular purpose or estimating the full model. From this estimation, we empirically affirm that there is a positive structural relationship between students’ after-school exercise hours and their academic performance exam scores, whereas the ordinary least squares method consistently estimates a negative relationship.

scholarly journals Parameter Estimation of Induction Machine at Standstill Using Two-Stage Recursive Least Squares Method

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jinliang Zhang ◽  
Longyun Kang ◽  
Lingyu Chen ◽  
Zhihui Xu
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Least Squares Method ◽  
Induction Machine ◽  
Recursive Least Squares ◽  
Two Stage ◽  
Electric Parameter ◽  
Rls Algorithm ◽  
Effective Implementation ◽  
Recursive Least Squares Method

This paper presents a two-stage recursive least squares (TSRLS) algorithm for the electric parameter estimation of the induction machine (IM) at standstill. The basic idea of this novel algorithm is to decouple an identifying system into two subsystems by using decomposition technique and identify the parameters of each subsystem, respectively. The TSRLS is an effective implementation of the recursive least squares (RLS). Compared with the conventional (RLS) algorithm, the TSRLS reduces the number of arithmetic operations. Experimental results verify the effectiveness of the proposed TSRLS algorithm for parameter estimation of IMs.

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