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• W2564393086 abstract "Given a sufficient statistic for a parametric family of distributions, one can estimate the parameter without access to the data. However, the memory or code size for storing the sufficient statistic may nonetheless still be prohibitive. Indeed, for $n$ independent samples drawn from a $k$-nomial distribution with $d=k-1$ degrees of freedom, the length of the code scales as $dlog n+O(1)$. In many applications, we may not have a useful notion of sufficient statistics (e.g., when the parametric family is not an exponential family) and we also may not need to reconstruct the generating distribution exactly. By adopting a Shannon-theoretic approach in which we allow a small error in estimating the generating distribution, we construct various {em approximate sufficient statistics} and show that the code length can be reduced to $frac{d}{2}log n+O(1)$. We consider errors measured according to the relative entropy and variational distance criteria. For the code constructions, we leverage Rissanen's minimum description length principle, which yields a non-vanishing error measured according to the relative entropy. For the converse parts, we use Clarke and Barron's formula for the relative entropy of a parametrized distribution and the corresponding mixture distribution. However, this method only yields a weak converse for the variational distance. We develop new techniques to achieve vanishing errors and we also prove strong converses. The latter means that even if the code is allowed to have a non-vanishing error, its length must still be at least $frac{d}{2}log n$." @default.
• W2564393086 created "2017-01-06" @default.
• W2564393086 creator A5024634142 @default.
• W2564393086 creator A5058345431 @default.
• W2564393086 date "2016-12-08" @default.
• W2564393086 modified "2023-10-16" @default.
• W2564393086 title "Minimum Rates of Approximate Sufficient Statistics" @default.
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• W2564393086 doi "https://doi.org/10.48550/arxiv.1612.02542" @default.
• W2564393086 hasPublicationYear "2016" @default.
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