One universally efficient estimation of the first-order autoregressive parameter and universal data compression
N Merhav, J Ziv - IEEE transactions on information theory, 1990 - ieeexplore.ieee.org
A universal nearly efficient estimator is proposed for the first-order autoregressive (AR)
model where the probability distribution of the driving noise is unknown. It is shown that
universal estimators for the AR model can be derived from universal data compression
algorithms and universal tests for randomness. In other words, estimators derived
appropriately from efficient universal codes can be expected to inherit good estimation
performance under some conditions. The proposed estimator has a simple information …
model where the probability distribution of the driving noise is unknown. It is shown that
universal estimators for the AR model can be derived from universal data compression
algorithms and universal tests for randomness. In other words, estimators derived
appropriately from efficient universal codes can be expected to inherit good estimation
performance under some conditions. The proposed estimator has a simple information …
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