Adaptation and Personalization, Vol. 1, Issue 1, Dec  2019, Pages 1-20; DOI: 10.31058/j.adp.2019.11001 10.31058/j.adp.2019.11001

Semi-mixture Regression Model for Incomplete Data

, Vol. 1, Issue 1, Dec  2019, Pages 1-20.

DOI: 10.31058/j.adp.2019.11001

Loc Nguyen 1* , Anum Shafiq 1

1 Advisory Board, Loc Nguyen’s Academic Network, An Giang, Vietnam

2 Department of Mathematics and Statistics, Preston University Islamabad, Islamabad, Pakistan

Received: 6 September 2018; Accepted: 16 October 2018; Published: 29 January 2019

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The regression expectation maximization (REM) algorithm, which is a variant of expectation maximization (EM) algorithm, uses parallelly a long regression model and many short regression models to solve the problem of incomplete data. Experimental results proved resistance of REM to incomplete data, in which accuracy of REM decreases insignificantly when data sample is made sparse with loss ratios up to 80%. However, the convergence speed of REM can be decreased if there are many independent variables. In this research, we use mixture model to decompose REM into many partial regression models. These partial regression models are then unified in the so-called semi-mixture regression model. Our proposed algorithm is called semi-mixture regression expectation maximization (SREM) algorithm because it is combination of mixture model and REM algorithm, but it does not implement totally the mixture model. In other words, only mixture coefficients in SREM are estimated according to mixture model whereas regression coefficients are estimated by REM. The experimental results show that SREM converges faster than REM does although the accuracy of SREM is not better than the accuracy of REM in fair tests.


Regression Model, Mixture Regression Model, Expectation Maximization Algorithm, Incomplete Data


© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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