AbstractFor many typical instances where Monte Carlo methods are applied attempts were made to find unbiased estimators, since for them the Monte Carlo error reduces to the statistical error. These problems usually take values in the scalar field. If we study vector valued Monte Carlo methods, then we are confronted with the question of whether there can exist unbiased estimators. This problem is apparently new. Below it is settled precisely. Partial answers are given, indicating relations to several classes of linear operators in Banach spaces
We construct minimum variance unbiased estimators of von Mises functionals in estimation problems wh...
We address the problem of existence of unbiased constrained parameter estimators. We show that if th...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased...
For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased...
For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased...
Multilevel Monte Carlo (MLMC) and recently proposed unbiased estimators are closely related. This co...
We provide a general methodology for unbiased estimation for intractable stochastic models. We consi...
We provide a general methodology for unbiased estimation for intractable stochastic models. We consi...
AbstractBased on the Bochner integral a concept of unbiasedness for set-valued estimators is introdu...
Methodological developments in computational statistics rely heavily on the use of unbiased estimati...
We introduce a new class of Monte Carlo-based approximations of expectations of random variables suc...
Best linear unbiased estimators (BLUE’s) are known to be optimal in many respects under normal assum...
It is observed that unbiased estimators are always inadmissible when the parameter (or function of t...
The concept of the linearity of estimators in finite population inference is not well defined. We pr...
We construct minimum variance unbiased estimators of von Mises functionals in estimation problems wh...
We address the problem of existence of unbiased constrained parameter estimators. We show that if th...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased...
For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased...
For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased...
Multilevel Monte Carlo (MLMC) and recently proposed unbiased estimators are closely related. This co...
We provide a general methodology for unbiased estimation for intractable stochastic models. We consi...
We provide a general methodology for unbiased estimation for intractable stochastic models. We consi...
AbstractBased on the Bochner integral a concept of unbiasedness for set-valued estimators is introdu...
Methodological developments in computational statistics rely heavily on the use of unbiased estimati...
We introduce a new class of Monte Carlo-based approximations of expectations of random variables suc...
Best linear unbiased estimators (BLUE’s) are known to be optimal in many respects under normal assum...
It is observed that unbiased estimators are always inadmissible when the parameter (or function of t...
The concept of the linearity of estimators in finite population inference is not well defined. We pr...
We construct minimum variance unbiased estimators of von Mises functionals in estimation problems wh...
We address the problem of existence of unbiased constrained parameter estimators. We show that if th...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...