Abstract
This paper presents an algorithm for estimation of statistical parameters of a population from stochastic samples. These samples are strongly disturbed in time and the objective of the algorithm is to estimate the characteristics of the population (mean vector and covariance matrix of distribution of the population).
The estimation technique is based on the modified maximum likelihood method and the observation of the population is carried out in a multi-dimensional space. Some simulation examples are given to show the performance of the algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M.L. Tiku (1967) “Estimating the mean and standard deviation from a censored normal sample.” Biometrica, 54, 1 and 2, p 155.
K.J. Aström (1980) “Maximum likelihood and prediction error method.” Automatica, 16, 551–574.
S. Puthenpura and N.K. Sinha (1986) “Modified maximum likelihood method for the robust estimation of system parameters from very noisy data.” Automatica, vol.22, no.2, 231–235.
M.L. Tiku (1980) “Robustness of MML estimators based on censored samples and robust test statistics.” J.Statist.Plan.Inference, 4, 123–143.
M.L. Tiku and M. Singh (1982) “Robust Statistics for testing mean vectors of multivariate distribution.” Commun. Statist. Theory. Meth., 11, 985–1001.
T.W. Anderson (1958) Introduction to multivariate statistics. John Wiley, N.Y.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Zeng, X., Vasseur, C. (1991). Estimation of Statistical Parameters of a Population from Very Noisy Samples. In: Tzafestas, S.G. (eds) Engineering Systems with Intelligence. Microprocessor-Based and Intelligent Systems Engineering, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-2560-4_77
Download citation
DOI: https://doi.org/10.1007/978-94-011-2560-4_77
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-5130-9
Online ISBN: 978-94-011-2560-4
eBook Packages: Springer Book Archive