Journal ArticleDOI
On estimation with weighted balanced-type loss function
TLDR
In this article, a balanced loss function for estimating an unknown parameter θ is introduced and various examples are given with regards to the issues of admissibility, dominance, Bayesianity, and minimaxity.About:
This article is published in Statistics & Probability Letters.The article was published on 2006-04-15. It has received 45 citations till now. The article focuses on the topics: Minimax estimator & Estimator.read more
Citations
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Journal ArticleDOI
Bayes estimation based on k-record data from a general class of distributions under balanced type loss functions
TL;DR: In this article, a semi-parametric class of distributions including exponential, Weibull, Pareto, Burr type XII and so on is considered, and the results are presented under the balanced versions of two well-known loss functions, namely squared error loss (SEL) and Varian's linear-exponential (LINEX) loss.
Journal ArticleDOI
Improving on the minimum risk equivariant estimator of a location parameter which is constrained to an interval or a half-interval
TL;DR: In this article, it was shown that the Bayes estimator with respect to a uniform prior on (a, ∞) is a minimax estimator which dominates the benchmark minimum risk equivariant estimator.
Journal ArticleDOI
Bayesian and Robust Bayesian analysis under a general class of balanced loss functions
TL;DR: For estimating an unknown parameter θ, the authors introduce and motivate the use of balanced loss functions of the form $${L_{\rho, \omega, \delta_0}(\theta, δ)+ (1-\omega) \rho(theta) \in[0,1], and q(·) is a positive weight function.
Journal ArticleDOI
Prediction of k-records from a general class of distributions under balanced type loss functions
TL;DR: In this article, the problem of predicting future k-records based on k-record data for a large class of distributions, which includes several well-known distributions such as: Exponential, Weibull (one parameter), Pareto, Burr type XII, among others, was investigated.
Journal ArticleDOI
Preliminary test and Stein estimations in simultaneous linear equations
TL;DR: In this paper, the authors formulate the simultaneous equation models into the problem of estimating the regression parameters of a multiple regression model, under elliptical errors, and define five different sorts of estimators for the vector-parameter.
References
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Book
Theory of point estimation
TL;DR: In this paper, the authors present an approach for estimating the average risk of a risk-optimal risk maximization algorithm for a set of risk-maximization objectives, including maximalaxity and admissibility.
Journal ArticleDOI
Estimating a Bounded Normal Mean
TL;DR: In this article, it was shown that if the interval is small (approximately two standard deviations wide) then the Bayes rule against a two point prior is the unique minimax estimator under squared error loss.
Book ChapterDOI
Bayesian and Non-Bayesian Estimation Using Balanced Loss Functions
TL;DR: It is concluded that BLFs and their associated optimal estimates will probably be useful in many estimation problems.
Journal ArticleDOI
On estimation with balanced loss functions
TL;DR: Gupta et al. as mentioned in this paper studied the notion of balanced loss in the context of a general linear model to reflect both goodness of fit and precision of estimation and showed that frequentist and Bayesian results for balanced loss follow from and also imply related results for quadratic loss functions reflecting only precision of estimations.
Related Papers (5)
Weighted balanced loss function and estimation of the mean time to failure
Josemar Rodrigues,Arnold Zellner +1 more