Loss Reduction in Point Estimation Problems
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Hans-Dieter Heike
Abstract
When evaluating point estimators by means of general loss functions, the expected loss is not always minimal, similar to the case of mean-biased estimators, whose mean squared error can be reduced by accounting for the mean-bias. Depending on the loss function, the socalled Lehmann-bias can be significantly more important than the mean-bias of an estimator. Although a simple decomposition does not hold for expected losses as it does for the mean squared error, the expected loss can still be reduced by correcting for the Lehmann-bias. An asymptotic and a bootstrap-based correction are suggested and compared in small samples for the exponential distribution by means of Monte Carlo simulation.
© Heldermann Verlag
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Articles in the same Issue
- A Note on Run Length Variability Reduction for EWMA Charting
- A Note on Nature and Significance of Stochastic Models
- Estimation of Pareto Survival Function in the Presence of Outlying Observations
- Loss Reduction in Point Estimation Problems
- Application of Arithmetic-Geometric Mean Inequality for Construction of Reliability Test Plan for Parallel Systems in the Presence of Covariates
- On Bayesian Estimation in a Parallel System
- Approximating Reliability of a System with Doubly Bounded Performance Functions
- Probability Weighted Moments Approach to Quality Control Charts
- Weibull Extension of Bivariate Exponential Regression Model with Gamma Frailty for Survival Data
- Nonlinear Tobit Decomposition
- Reliability Test Plans for Exponentiated Log-Logistic Distribution
- Transient Analysis of a System with Queue Dependent Servers and Controllable Arrivals