Parameter Estimation for a Multiplicative Competitive Interaction Model—Least Squares Approach

1974 ◽  
Vol 11 (3) ◽  
pp. 303-311 ◽  
Author(s):  
Masao Nakanishi ◽  
Lee G. Cooper
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Interaction Model ◽  
Multiplicative Model ◽  
Competitive Interaction ◽  
Least Squares Estimation ◽  
Potential Usefulness ◽  
Estimation Techniques ◽  
Model Based ◽  
Least Squares Approach

Least squares estimation techniques are developed for a special multiplicative model based on the Luce choice axiom whose potential usefulness in marketing applications justifies estimation techniques which can be easily implemented.

SYSTOLIC ALGORITHMS FOR RECURSIVE TOTAL LEAST SQUARES PARAMETER ESTIMATION AND MIXED RLS/RTLS PROBLEMS

1993 ◽  
Vol 04 (01) ◽  
pp. 55-68 ◽  
Author(s):  
MARC MOONEN
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Total Least Squares ◽  
Parallel Architectures ◽  
Least Squares Estimation ◽  
Recursive Least Squares ◽  
Recursive Algorithms ◽  
Least Squares Problems ◽  
Linearly Constrained ◽  
Systolic Algorithms

Total least squares parameter estimation is an alternative to least squares estimation though much less used in practice, partly due to the absence of efficient recursive algorithms or parallel architectures. Here it is shown how previously developed systolic algorithms/architectures for recursive least squares estimation can be used for recursive total least squares problems. Unconstrained as well as linearly constrained and "mixed RLS/RTLS" problems are considered.

GM(1,1) Model Based on Least Absolute Estimation of Residual

2011 ◽  
Vol 90-93 ◽  
pp. 2881-2886
Author(s):  
Yong He Deng
Keyword(s):  
Least Squares ◽  
Least Squares Estimation ◽  
Absolute Value ◽  
Model Based ◽  
Gross Error

Based on the choice characteristic of least absolute estimation ,and the principle of the minimum of summation of residual absolute value,the GM(1,1) model is proposed to distortion inspecting and forecasting.Exemples show the precision of least absolute estimation is uncertain to be higher a little than that of least squares estimation when observation value contains gross error,and the precision of least absolute estimation is lower a little than that of least squares estimation when observation value doesn’t contain gross error.So least absolute estimation isn’t beter to GM(1,1).

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