Some Remarks on Improving Unbiased Estimators by Multiplication with a Constant

1985 ◽  
pp. 150-161 ◽  
Author(s):  
J. Kleffe
Keyword(s):  
Unbiased Estimators

scholarly journals Unbiased estimation of the solution to Zakai’s equation

2020 ◽  
Vol 26 (2) ◽  
pp. 113-129
Author(s):  
Hamza M. Ruzayqat ◽  
Ajay Jasra
Keyword(s):  
Continuous Time ◽  
Unbiased Estimation ◽  
Finite Variance ◽  
Linear Filtering ◽  
Multilevel Monte Carlo ◽  
Zakai Equation ◽  
Unbiased Estimators ◽  
First Order ◽  
Multilevel Monte Carlo Method ◽  
Filtering Problem

AbstractIn the following article, we consider the non-linear filtering problem in continuous time and in particular the solution to Zakai’s equation or the normalizing constant. We develop a methodology to produce finite variance, almost surely unbiased estimators of the solution to Zakai’s equation. That is, given access to only a first-order discretization of solution to the Zakai equation, we present a method which can remove this discretization bias. The approach, under assumptions, is proved to have finite variance and is numerically compared to using a particular multilevel Monte Carlo method.

Some Easily Found Minimum Variance Unbiased Estimators

1978 ◽  
Vol 32 (1) ◽  
pp. 29 ◽  
Author(s):  
William C. Guenther
Keyword(s):  
Minimum Variance ◽  
Unbiased Estimators

scholarly journals Statistical properties of groundwater noble gas paleoclimate models: Are they robust and unbiased estimators?

2010 ◽  
Vol 11 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Tie Sun ◽  
Chris M. Hall ◽  
Maria Clara Castro
Keyword(s):  
Noble Gas ◽  
Statistical Properties ◽  
Unbiased Estimators

A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

2013 ◽  
Vol 1 (1) ◽  
pp. 135-154 ◽  
Author(s):  
Peter M. Aronow ◽  
Joel A. Middleton
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Covariate Adjustment ◽  
Randomized Experiments ◽  
Treatment Assignment ◽  
Unbiased Estimators ◽  
Average Treatment

AbstractWe derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

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