Using Nonlinear Models to Enhance Prediction Performance with Incomplete Data

Faraj A. A. Bashir, Hua-Liang Wei

2015

Abstract

A great deal of recent methodological research on missing data analysis has focused on model parameter estimation using modern statistical methods such as maximum likelihood and multiple imputation. These approaches are better than traditional methods (for example listwise deletion and mean imputation methods). These modern techniques can lead to unbiased parametric estimation in many particular application cases. However, these methods do not work well in some cases especially for nonlinear systems that have highly nonlinear behaviour. This paper explains the linear parametric estimation in existence of missing data, which includes an overview of biased and unbiased linear parametric estimation with missing data, and provides accessible descriptions of expectation maximization (EM) algorithm and Gauss-Newton method. In particular, this paper proposes a Gauss-Newton iteration method for nonlinear parametric estimation in case of missing data. Since Gauss-Newton method needs initial values that are hard to obtain in the presence of missing data, the EM algorithm is thus used to estimate these initial values. In addition, we present two analysis examples to illustrate the performance of the proposed methods.

Paper Citation

in Harvard Style

A. A. Bashir F. and Wei H. (2015). Using Nonlinear Models to Enhance Prediction Performance with Incomplete Data . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 141-148. DOI: 10.5220/0005157201410148

in Bibtex Style

@conference{icpram15,
author={Faraj A. A. Bashir and Hua-Liang Wei},
title={Using Nonlinear Models to Enhance Prediction Performance with Incomplete Data},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2015},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005157201410148},
isbn={978-989-758-076-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Using Nonlinear Models to Enhance Prediction Performance with Incomplete Data
SN - 978-989-758-076-5
AU - A. A. Bashir F.
AU - Wei H.
PY - 2015
SP - 141
EP - 148
DO - 10.5220/0005157201410148