From Parameter Estimation to Dispersion of Nonstationary Gauss-Markov Processes

This paper provides a precise error analysis for the maximum likelihood estimate â(u) of the parameter a given samples u = (u
₁
, ... , u
_n
)T drawn from a nonstationary Gauss-Markov process U
_i
= αU
_i-1
+ Z
_i
, i ≥ 1, where α > 1, U
₀
= 0, and Zz's are independent Gaussian random variables with zero mean and variance σ
²
. We show a tight nonasymptotic exponentially decaying bound on the tail probability of the estimation error. Unlike previous works, our bound is tight already for a sample size of the order of hundreds. We apply the new estimation bound to find the dispersion for lossy compression of nonstationary Gauss-Markov sources. We show that the dispersion is given by the same integral formula derived in our previous work [1] for the (asymptotically) stationary GaussMarkov sources, i.e., |α|<; 1. New ideas in the nonstationary case include a deeper understanding of the scaling of the maximum eigenvalue of the covariance matrix of the source sequence, and new techniques in the derivation of our estimation error bound.