Estimasi Parameter Model Moving Average Orde 1 Menggunakan Metode Momen dan Maximum Likelihood

Modified Maximum Likelihood Estimation in First-Order Autoregressive Moving Average Models with some Non-Normal Residuals

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...

Maximum Likelihood Identification of Gaussian Autoregressive Moving Average Models

Maximum Likelihood Parameter Estimation

The problem of estimating the parameters for continuous-time partially observed systems is discussed. New exact lters for obtaining Maximum Likelihood (ML) parameter estimates via the Expectation Maximization algorithm are derived. The methodology exploits relations between incomplete and complete data likelihood and gradient of likelihood functions, which are derived using Girsanov's measure t...

Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models

We discuss computational aspects of likelihood-based estimation of univariate ARFIMA(p, d, q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.

Maximum-Likelihood Circle-Parameter Estimation via Convolution

In this paper, we present an interpretation of the Maximum Likelihood Estimator (MLE) and the Delogne-Kåsa Estimator (DKE) for circle-parameter estimation via convolution. Under a certain model for theoretical images, this convolution is an exact description of the MLE. We use our convolution based MLE approach to find good starting estimates for the parameters of a circle, that is, the centre ...