Nonsmooth nonconvex optimization approach to clusterwise linear regression problems

2013 ◽  
Vol 229 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Adil M. Bagirov ◽  
Julien Ugon ◽  
Hijran Mirzayeva
Keyword(s):  
Linear Regression ◽  
Nonconvex Optimization ◽  
Optimization Approach ◽  
Nonsmooth Nonconvex Optimization ◽  
Regression Problems ◽  
Clusterwise Linear Regression

scholarly journals A maximum likelihood methodology for clusterwise linear regression

1988 ◽  
Vol 5 (2) ◽  
pp. 249-282 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
William L. Cron
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Clusterwise Linear Regression

MONTE CARLO METHODS IN FUZZY NON-LINEAR REGRESSION

2008 ◽  
Vol 04 (02) ◽  
pp. 123-141 ◽  
Author(s):  
AREEG ABDALLA ◽  
JAMES BUCKLEY
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Linear Regression ◽  
Fuzzy Numbers ◽  
Random Number ◽  
Random Number Generator ◽  
Search Space ◽  
Triangular Fuzzy Numbers ◽  
Non Linear ◽  
Regression Problems

We apply our new fuzzy Monte Carlo method to certain fuzzy non-linear regression problems to estimate the best solution. The best solution is a vector of triangular fuzzy numbers, for the fuzzy coefficients in the model, which minimizes an error measure. We use a quasi-random number generator to produce random sequences of these fuzzy vectors which uniformly fill the search space. We consider example problems to show that this Monte Carlo method obtains solutions comparable to those obtained by an evolutionary algorithm.

Missing Value Imputation via Clusterwise Linear Regression

2020 ◽  
pp. 1-1
Author(s):  
Napsu Karmitsa ◽  
Sona Taheri ◽  
Adil Bagirov ◽  
Pauliina Makinen
Keyword(s):  
Linear Regression ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Clusterwise Linear Regression
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