  Let $\{X_n: n\in \N\}$ be a linear process with density function $f(x)\in
L^2(\R)$. We study wavelet density estimation of $f(x)$. Under some regular
conditions on the characteristic function of innovations, we achieve, based on
the number of nonzero coefficients in the linear process, the minimax optimal
convergence rate of the integrated mean squared error of density estimation.
Considered wavelets have compact support and are twice continuously
differentiable. The number of vanishing moments of mother wavelet is
proportional to the number of nonzero coefficients in the linear process and to
the rate of decay of characteristic function of innovations. Theoretical
results are illustrated by simulation studies with innovations following
Gaussian, Cauchy and chi-squared distributions.
