Multivariate Probabilistic Wavelet Approximation

G. A. Anastassiou, S. T. Rachev and X. M. Yu
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G. A. Anastassiou: Memphis State University, Department of Mathematical Sciences
S. T. Rachev: University of California at Santa Barbara, Department of Mathematics
X. M. Yu: Southwest Missouri State University, Department of Mathematics

A chapter in Approximation, Probability, and Related Fields, 1994, pp 65-73 from Springer

Abstract: Abstract Multivariate probability laws are approximated by some naturally arising wavelet operations. They transform multivariate distribution functions to multivariable distribution functions. The degree of this approximation is estimated by establishing some sharp local Jackson type inequalities. We extend the results for distributions of stochastic processes and unbounded measures.

Keywords: Product Density; Bounded Measure; Uniform Distance; Wavelet Orthonormal Base; Vague Convergence (search for similar items in EconPapers)
Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-2494-6_4

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DOI: 10.1007/978-1-4615-2494-6_4

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