Deconvolving multivariate kernel density estimates from contaminated associated observations

  • Authors:

  • E. Masry

  • Affiliations:

  • Dept. of Electr. & Comput. Eng., Univ. of California, La Jolla, CA, USA

  • Venue:
  • IEEE Transactions on Information Theory
  • Year:
  • 2006

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Abstract

We consider the estimation of the multivariate probability density function f(x1,...,xp) of X1,...,Xp of a stationary positively or negatively associated (PA or NA) random process {Xi}i=1∞ from noisy observations. Both ordinary smooth and super smooth noise are considered. Quadratic mean and asymptotic normality results are established.