Maximum likelihood estimation of linear SISO models subject to missing output data and missing input data
2014 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 87, no 11, 2354-2364 p.Article in journal (Refereed) Published
In this paper we describe an approach to maximum likelihood estimation of linear single input single output (SISO) models when both input and output data are missing. The criterion minimised in the algorithms is the Euclidean norm of the prediction error vector scaled by a particular function of the covariance matrix of the observed output data. We also provide insight into when simpler and in general sub-optimal schemes are indeed optimal. The algorithm has been prototyped in MATLAB, and we report numerical results that support the theory.
Place, publisher, year, edition, pages
Taylor and Francis: STM, Behavioural Science and Public Health Titles , 2014. Vol. 87, no 11, 2354-2364 p.
system identification; maximum likelihood estimation; missing data
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-111470DOI: 10.1080/00207179.2014.913346ISI: 000341955300012OAI: oai:DiVA.org:liu-111470DiVA: diva2:756510