Consistency of the Plug-In Estimator of the Entropy Rate for Ergodic Processes

Abstract

A plug-in estimator of entropy is the entropy of the distribution where probabilities of symbols or blocks have been replaced with their relative frequencies in the sample. Consistency and asymptotic unbiasedness of the plug-in estimator can be easily demonstrated in the IID case. In this paper, we ask whether the plug-in estimator can be used for consistent estimation of the entropy rate $h$ of a stationary ergodic process. The answer is positive if, to estimate block entropy of order $k$, we use a sample longer than $2^{k(h+\epsilon)}$, whereas it is negative if we use a sample shorter than $2^{k(h-\epsilon)}$. In particular, if we do not know the entropy rate $h$, it is sufficient to use a sample of length $(|X|+\epsilon)^{k}$ where $|X|$ is the alphabet size. The result is derived using $k$-block coding. As a by-product of our technique, we also show that the block entropy of a stationary process is bounded above by a nonlinear function of the average block entropy of its ergodic components. This inequality can be used for an alternative proof of the known fact that the entropy rate a stationary process equals the average entropy rate of its ergodic components.