Title :
Multivariate probability density deconvolution for stationary random processes
Author_Institution :
Dept. of Electr. Eng., California Univ., San Diego, La Jolla, CA, USA
fDate :
7/1/1991 12:00:00 AM
Abstract :
The kernel-type estimation of the joint probability density functions of stationary random processes from noisy observations is considered. Precise asymptotic expressions and bounds on the mean-square estimation error are established, along with rates of mean-square convergence, for processes satisfying a variety of mixing conditions. The dependence of the convergence rates on the joint density of the noise process is studied
Keywords :
convergence; information theory; probability; random processes; convergence rates; deconvolution; joint probability density functions; kernel-type estimation; mean-square convergence; mean-square estimation error; multivariate probability density; noisy observations; stationary random processes; Additive noise; Convergence; Convolution; Deconvolution; Density functional theory; Estimation error; Kernel; Probability density function; Random processes; Random variables;
Journal_Title :
Information Theory, IEEE Transactions on
