Results 241 to 250 of about 6,072 (305)

Unbiased minimum-variance linear state estimation

Automatica, 1987
A method is developed for linear estimation in the presence of unknown or highly non-Gaussian system inputs. The state update is determined so that it is unaffected by the unknown inputs. The filter may not be globally optimum in the mean square error sense. However, it performs well when the unknown inputs take extreme or unexpected values.
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Uniformly minimum variance unbiased estimators of Theil—Tornqvist index numbers

Economics Letters, 1992
Abstract The Theil-Tornqvist index number is widely used for pairwise comparisons of cost-of-living over time periods or geographical regions/countries. Traditionally this index is used in its log-change form as well as in a multiplicative form, with a one-to-one correspondence between the two forms. In this note we examine the sampling distributions
D. S. Prasada Rao, Eliyathamby A. Selvanathan   +1 more
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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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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 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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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 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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Unbiased minimum variance estimator design for scalar quadratic maps

Proceedings of the 2005, American Control Conference, 2005., 2005
In this paper, we consider the state estimation problem for scalar discrete-time nonlinear systems with second degree polynomial nonlinearities. This research is a follow up to our previous work on suboptimal minimum variance estimator design for quadratic maps.
null Tongyan Zhai, E.E. Yaz, null Huawei Ruan   +2 more
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Uniformly minimum variance unbiased estimators in split plot designs

Biometrical Journal, 1980
AbstractExplicit forms of the uniformly minimum variance unbiased estimators (UMVUE) of estimable linear functions of the fixed effects and the UMVUE of variance components in the fixed linear normal model of the complete randomized block design in which there are several levels of plots splitting are found.
Henryk Mikos
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Learning Minimum Variance Unbiased Estimators

2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2022
Tzvi Diskin, Yonina C. Eldar, Ami Wiesel
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