Latent variable models and factors analysis
Latent variable models and factors analysis
Time series: data analysis and theory
Time series: data analysis and theory
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Convergence assessment techniques for Markov chain Monte Carlo
Statistics and Computing
Pairwise likelihood inference for ordinal categorical time series
Computational Statistics & Data Analysis
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We consider computationally-fast methods for estimating parameters in ARMA processes from binary time series data, obtained by thresholding the latent ARMA process. All methods involve matching estimated and expected autocorrelations of the binary series. In particular, we focus on the spectral representation of the likelihood of an ARMA process and derive a restricted form of this likelihood, which uses correlations at only the first few lags. We contrast these methods with an efficient but computationally-intensive Markov chain Monte Carlo (MCMC) method. In a simulation study we show that, for a range of ARMA processes, the spectral method is more efficient than variants of least squares and much faster than MCMC. We illustrate by fitting an ARMA(2,1) model to a binary time series of cow feeding data.