article
Maximum probability estimation for an autoregressive process
International Journal of Statistics and Applied Mathematics · 2017 · Vol. 2(6) · pp. 122–123
Abstract
We observe Xi, …., Xn, where Xi = θi Xi – 2 x Yi, ---------- (1) Where Xθ is defined as zero, and Yi,…., Yn are unobservable random variables, independent, each normal with mean zero and variance θ2. θ1 and θ2 are both unknown and are to be estimated by using Maximum Probability Estimation. It is shown that the maximum likelihood estimators of the parameters have certain optimal properties.
Fault Detection and Control SystemsMathematicsAutoregressive modelEstimatorUnobservableMaximum likelihoodZero (linguistics)StatisticsSTAR modelMaximum likelihood sequence estimationRandom variable
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