Results 241 to 250 of about 6,008 (306)

A Note on Minimum Variance Unbiased Estimation

Communications in Statistics - Theory and Methods, 2010
The process of Rao–Blackwellization involves specification of a naive unbiased estimator of τ(θ) and then determination of its conditional expectation given a complete and sufficient statistic T for θ. This leads to the minimum variance unbiased estimator (MVUE) U ≡ U(T) for τ(θ).
Nitis Mukhopadhyay, Debanjan Bhattacharjee   +1 more
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Minimum variance unbiased estimation in the presence of an adversary

2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017
Consider a setup in which a central estimator seeks to estimate an unknown deterministic parameter using measurements from multiple sensors. Some of the sensors may be adversarial in that their utility increases with the Euclidean distance between the estimate of the central estimator and their own local estimate.
Kewei Chen, Vijay Gupta, Yih-Fang Huang
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Minimum Variance Unbiased Estimation of Software Reliability

Probability in the Engineering and Informational Sciences, 1989
As the formal methods of proving correctness of a computer program are still very inadequate, in practice when a new piece of software is developed and all obvious errors are removed, it is tested with different (random) inputs in order to detect the remaining errors and assess its quality.
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Minimum Variance Unbiased Estimate of a Coverage Probability

Operations Research, 1968
Let F(x, θ) denote the distribution function of a vector variate x, whose range does not depend on the parameter θ. Then assuming that θ admits a complete and sufficient estimator θ̂, we derive minimum variance unbiased estimate of Px ≦ a= F(a, θ), where a is a known vector. The result is applied to a coverage problem in a normal set up.
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Minimum Variance Linear Unbiased Estimators

2013
We give definitions and examples of the best (in the sense of minimum variance) unbiased estimators of the location and scale parameters, based on linear combinations of order statistics.
Mohammad Ahsanullah, Valery Nevzorov, Mohammad Shakil   +2 more
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MINIMUM VARIANCE UNBIASED ESTIMATION OF PROBABILITY DENSITIES

Australian Journal of Statistics, 1980
SummaryThe purpose of this note is to point out an elementary method for deriving the minimum variance unbiased (mvu) estimators of probability densities. The method is illustrated by estimating the densities of some well known important distributions.
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Minimum Variance Unbiased Estimators for Poisson Probabilities

Technometrics, 1962
This paper considers the problem of estimating Poisson probabilities or relative frequencies and some extensions of that problem. It is shown how minimum variance unbiased estimators based on a simple random sample of 72 observations on a Poisson process may be easily developed. Variances of the estimators and estimators for their variances are derived.
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Amounts of Information and the Minimum Variance Unbiased Estimation

1995
In the previous chapter we introduced the concept of the one-directional distribution and discussed the locally minimum variance unbiased estimation for its family. In this section, from another point of view we shall treat the case when the amount of Fisher information is infinity and show that the locally minimum variance of unbiased estimators is ...
Masafumi Akahira, Kei Takeuchi
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Some Examples of Minimum Variance Unbiased Estimates

The American Statistician, 1972
Tenenbein (1971) presented an interesting example in which he constructed a minimum variance unbiased estimate of a certain integer valued parameter. His example is reviewed in the light of the fact that his "estimate" takes on non-integer values. Below, in such a case, the term "estimate" is reserved for integervalued functions of the observations ...
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Minimum variance unbiased estimation based on bootstrap iterations

Statistics and Computing, 2006
Practical computation of the minimum variance unbiased estimator (MVUE) is often a difficult, if not impossible, task, even though general theory assures its existence under regularity conditions. We propose a new approach based on iterative bootstrap bias correction of the maximum likelihood estimator to accurately approximate the MVUE.
Lee, SMS, Chan, KYF, Ng, KW
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