DocumentCode
1664679
Title
Maximum likelihood estimation of time-series with Markov regime
Author
Dey, Shuvashis ; Krishnamurthy, Vikram
Author_Institution
Dept. of Syst. Eng., Australian Nat. Univ., Canberra, ACT
Volume
3
fYear
1994
Firstpage
2856
Abstract
In this paper, we consider the estimation of various Markov-modulated time-series. We obtain maximum likelihood estimates of the time-series parameters including the Markov chain transition probabilities and the time-series coefficients using the expectation maximization (EM) algorithm. Also the recursive EM algorithm is used to obtain online parameter estimates. Simulation studies show that both algorithms yield satisfactory results
Keywords
Markov processes; estimation theory; maximum likelihood estimation; optimisation; parameter estimation; probability; time series; Markov chain; Markov regime; expectation maximization; maximum likelihood estimation; parameter estimation; time-series; transition probability; Delay; Integrated circuit modeling; Iterative algorithms; Maximum likelihood estimation; Parameter estimation; Polynomials; Recursive estimation; Switches; Systems engineering and theory; Yttrium;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location
Lake Buena Vista, FL
Print_ISBN
0-7803-1968-0
Type
conf
DOI
10.1109/CDC.1994.411365
Filename
411365
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