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Prakash Patil

dblp:47/3863 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1994
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › estimation theory › nonparametric estimation
bandwidth selection
0.011994
On the Efficiency of On-Line Density Estimators · IEEE Trans. Inf. Theory 1994
Information theory › estimation theory
density estimation
0.011994
On the Efficiency of On-Line Density Estimators · IEEE Trans. Inf. Theory 1994

Methods — techniques the papers use, named apart from their topics

recursive estimation · 0.0empirical bandwidth selection · 0.0
YearPublicationVenuePosition
1994 On the Efficiency of On-Line Density Estimators
abstract
An on-line density estimator may be defined to be one where each update, following the arrival of a new data value, may be accomplished after no more than a fixed number of calculations. This definition should also apply to any empirical bandwidth selection rule for such an estimator. Recursive estimators comprise only a special case of on-line estimators, but even there, on-line bandwidth formulas have not been developed. The authors introduce a class of on-line estimators, and discuss efficiency in this context. It is shown that some nonrecursive members of the class achieve greater efficiency than any recursive estimators, and that efficiency increases to 100% as the order of the estimated derivative increases. On-line bandwidth selection rules, enabling these high orders of efficiency to be achieved asymptotically, are introduced.>
Prakash Patil
IEEE Trans. Inf. Theory2