Results 281 to 290 of about 74,320 (358)

Learning Minimum Variance Unbiased Estimators

International Conference on Security and Management, 2022
The Gauss-Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non-linear settings via deep learning
Tzvi Diskin, Yonina C. Eldar, A. Wiesel
semanticscholar   +1 more source

Bayes quadratic unbiased estimator of variance component in multi-samples repeated measurements ANOVA Model (Multi-RMM)

Journal of Statistics & Management Systems, 2022
In this paper, we present the best quadratic unbiased estimator of variance components for unbalanced data for linear repeated measurement model (RMM).
Jasim .Mohammed Ali Al-Isawi, A. S. Al-Mouel, Ali Hasan Ali   +2 more
semanticscholar   +1 more source

The global minimum variance unbiased estimator of the parameter for a truncated parameter family under the optimal ranked set sampling

Journal of Statistical Computation and Simulation, 2018
The minimum variance unbiased estimators (MVUEs) of the parameters for various distributions are extensively studied under ranked set sampling (RSS). However, the results in existing literatures are only locally MVUEs, i.e.
Wangxue Chen, Yi Tian, M. Xie
semanticscholar   +1 more source

Combined Road Roughness and Vehicle Parameter Estimation Based on a Minimum Variance Unbiased Estimator

, 2019
This paper deals with the simultaneous identification of road roughness and vehicle parameters, considering the effect of vehicle–structure interaction.
O. Shereena, B. Rao
semanticscholar   +1 more source

Computation of the uniform minimum variance unbiased estimator of a normal mean following a group sequential trial.

Computers and biomedical research, an international journal, 1993
The sampling distribution of data collected in a group sequential trial is such that the usual fixed-sample estimates of treatment effect are biased. Improved estimates can be obtained by taking the group sequential stopping rule into account.
S. Emerson
semanticscholar   +1 more source

A generalized improvement procedure for variance bounds for minimum variance unbiased estimator in inverse sampling

, 1983
New bounds are obtained for the variance of the minimum variance unbiased estimator of p i n inverse sampling. A generalized procedure for further improving the bounds is also discussed.
Ramesh M. Korwa, G. Prasad, A. Sahai
semanticscholar   +1 more source

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.
Valery B. Nevzorov, Mohammad Ahsanullah, Mohammad Shakil   +2 more
openaire   +2 more sources

Inadmissibility of the uniformly minimum variance unbiased estimator of the inverse Gaussian variance

, 1990
The uniformly minimum variance unbiased estimator (UMVUE) of the variance of the inverse Gaussian distribution is shown to be inadmissible in terms of the mean squared error, and a dominating estimator is given.
H. K. Hseih, R. M. Korwar
semanticscholar   +1 more source

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
openaire   +4 more sources

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.
openaire   +2 more sources