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Quantization of probability distributions under norm-based distortion measures
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Sylvain Delattre
Veröffentlicht/Copyright:
25. September 2009
Summury
For a probability measure P on Rd and n ∊ N consider en = inf ∫ mina∊αV(||x − a||)dP(x) where the infimum is taken over all subsets α of Rd with card(α) ≤ n and V is a nondecreasing function. Under certain conditions on V, we derive the precise n-asymptotics of en for nonsingular distributions P and we find the asymptotic performance of optimal quantizers using weighted empirical measures.
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Published Online: 2009-09-25
Published in Print: 2004-04-01
© R. Oldenbourg Verlag, München
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Artikel in diesem Heft
- Quantization of probability distributions under norm-based distortion measures
- Confidence estimation of the covariance function of stationary and locally stationary processes
- Efficient estimation of a linear functional of a bivariate distribution with equal, but unknown, marginals: The minimum chi-square approach
- Locally asymptotically optimal tests in semiparametric generalized linear models in the 2-sample-problem
- Maximum likelihood estimator in a two-phase nonlinear random regression model
Artikel in diesem Heft
- Quantization of probability distributions under norm-based distortion measures
- Confidence estimation of the covariance function of stationary and locally stationary processes
- Efficient estimation of a linear functional of a bivariate distribution with equal, but unknown, marginals: The minimum chi-square approach
- Locally asymptotically optimal tests in semiparametric generalized linear models in the 2-sample-problem
- Maximum likelihood estimator in a two-phase nonlinear random regression model
