Asymptotic Expansions of the Distributions of Estimators in a Linear Functional Relationship and Simultaneous Equations

1980 ◽  
Vol 75 (371) ◽  
pp. 693-700 ◽  
Author(s):  
Naoto Kunitomo
Keyword(s):  
Asymptotic Expansions ◽  
Linear Functional ◽  
Functional Relationship ◽  
Simultaneous Equations

Inference in a linear functional relationship with replications

2021 ◽  
Vol 35 (3) ◽  
Author(s):  
Julio Hokama ◽  
Pedro Morettin ◽  
Heleno Bolfarine ◽  
Manuel Galea
Keyword(s):  
Linear Functional ◽  
Functional Relationship

A Variable Step Size LMS Adaptive Filtering Algorithm and Simulations Based on Computational Verb Theory

2013 ◽  
Vol 756-759 ◽  
pp. 3972-3976 ◽  
Author(s):  
Li Hui Sun ◽  
Bao Yu Zheng
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Linear Functional ◽  
Functional Relationship ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Variable Step ◽  
Simulation Results

Based on traditional LMS algorithm, variable step LMS algorithm and the analysis for improved algorithm, a new variable step adaptive algorithm based on computational verb theory is put forward. A kind of sectorial linear functional relationship is established between step parameters and the error. The simulation results show that the algorithm has the advantage of slow change which is closely to zero. And overcome the defects of some variable step size LMS algorithm in adaptive steady state value is too large.

An approximate conditional analysis of the linear functional relationship

1987 ◽  
Vol 28 (1) ◽  
pp. 183-202 ◽  
Author(s):  
H. Schneeweiß ◽  
D. A. Sprott ◽  
R. Viveros
Keyword(s):  
Linear Functional ◽  
Functional Relationship ◽  
Conditional Analysis

Approximate Distributions and Power of Test Statistics for Overidentifying Restrictions in a System of Simultaneous Equations

1988 ◽  
Vol 4 (2) ◽  
pp. 248-274 ◽  
Author(s):  
Naoto Kunitomo
Keyword(s):  
Asymptotic Expansions ◽  
Simultaneous Equations ◽  
Ratio Test ◽  
Test Statistics ◽  
Power Functions ◽  
Significance Level ◽  
Power Of Test ◽  
Normality Assumption ◽  
The Difference ◽  
Critical Regions

We derive asymptotic expansions of the distributions of test statistics for over-identifying restrictions in a system of simultaneous equations under the null and the non-null hypotheses. We investigate the effects of the normality assumption for disturbances on the test statistics based on their asymptotic expansions. We also study the power functions of test statistics based on their asymptotic expansions. After modifying their critical regions to the same significance level, the power function of Basmann's statistic is larger than that of the likelihood ratio test when the variance of disturbances is sufficiently small. However, the difference in powers of the two test statistics disappears as the sample size grows larger.

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