Linear Time Series Analysis
Methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.
Simulate General Linear Process
Autocorrelations to AR parameters
Durbin-Levinsion Loglikelihood
Prediction residuals
Simulate linear time series
Prediction variance
Simulate GLP given innovations
Inverse of Toeplitz matrix of order n+1 given inverse of order n
Minimum Mean Square Forecast
compute the matrix inverse of a positive-definite Toepliz matrix
Loglikelihood function of stationary time series using Trench algorithm
Exact MLE for mean given the autocorrelation function
Exact log-likelihood and MLE for variance
Nonparametric estimate of the innovation variance
test if argument is a symmetric Toeplitz matrix
theoretical autocovariance function (acvf) of ARMA
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