Estimation on a GAR(1) Process by the EM Algorithm
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Georgiana Popovici
Abstract
Because of the increasing number of interrelated processes continuous monitoring and controlling of processes get more and more important. Time series constitute one possibility of modelling processes in order to determine an appropriate monitoring policy. One major problem when deriving a time series consists of estimating the values of the relevant parameters. This paper deals with the estimation of the parameters of a first order autoregressive gamma process by means of the EM algorithm. The formulae of the EM sequence are derived, the convergence of the procedure is established and the results of a simulation study are presented. The EM algorithm proves to be an appropriate estimation procedure in the case of the complex statistical model represented by a GAR(1) process.
© Heldermann Verlag
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- Multivariate Information in Univariate Control Charts
- A Note on the Moments of Random Variables
- A Heuristic Approach for Constrained Redundancy Optimization in Multi-state Systems
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Articles in the same Issue
- Estimation on a GAR(1) Process by the EM Algorithm
- Point and Interval Estimation for the Lifetime Distribution of a k-Unit Parallel System Based on Progressively Type-II Censored Data
- A Note on the Distribution of Bousquet
- A Quality Index for Evaluating the Bank Capital Adequacy According to Basel I and II
- Reliability Test Plans for Series Systems in the Presence of Covariates
- Multivariate Information in Univariate Control Charts
- A Note on the Moments of Random Variables
- A Heuristic Approach for Constrained Redundancy Optimization in Multi-state Systems
- Reliability Computation of Moranda's Geometric Software Reliability Model
- Economic Design of A Modified Variable Sample Size and Sampling Interval Chart
- Gamma Frailty Regression Models in Mixture Distributions
- A Truncated Bivariate t Distribution
