Journal Article10.1007/S00184-017-0615-2
Estimation of the order restricted scale parameters for two populations from the Lomax distribution
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TL;DR: It is proved that the best equivariant estimators of the scale parameters (in the unrestricted case) are not admissible and estimators that improve upon the usual ones are constructed (when these parameters are known to be ordered).
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Abstract: The usual methods of estimating the unknown parameters of a distribution, use only the information given from the sample data. In many cases, there is, also, another important information for estimating the unknown parameters of our model, such as the order of these parameters, and this last information improves the quality of estimation. In this paper, we deal with the problem of estimating the ordered scale parameters from two populations of the multivariate Lomax distribution, with unknown location parameters. It is proved that the best equivariant estimators of the scale parameters (in the unrestricted case) are not admissible and we construct estimators that improve upon the usual ones (when these parameters are known to be ordered).
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Citations
Место и роль инновационных технологий на уроках математики
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- 01 Jan 2016
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Estimating the common hazard rate of two exponential distributions with ordered location parameters
TL;DR: In this paper, the hazard rate of two exponential distributions with unknown and ordered location parameters under a general class of bowl-shaped scale invariant distributions is estimated under a generalized class of scale invariants.
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Estimating the common hazard rate of several exponential distributions
Lakshmi Kanta Patra,Somesh Kumar +1 more
TL;DR: In this article, the estimation of the common hazard rate of several exponential distributions with unknown and unequal location parameters with a common scale was studied, and the hazard rate was estimated using a common hazard metric.
3
Statistical Inference for Lomax Model based on Inverse Moment Estimation Method
Fan Zhang
- 01 Sep 2020
TL;DR: In this article, the point and confidence interval estimation for the parameters of the two-parameter Lomax distribution were studied by using inverse moment method, and the exact confidence interval of the shape parameter and the approximate joint confidence region of shape with scale parameters were also obtained.
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•Posted Content
Componentwise Equivariant Estimation of Order Restricted Location and Scale Parameters In Bivariate Models: A Unified Study
Naresh Garg,Neeraj Misra +1 more
TL;DR: In this article, the authors consider a general bivariate model and general loss function and unify various results proved in the literature, and apply these results to various dependent bivariate models (bivariate normal, a bivariate exponential model based on a Morgenstern family copula, a BIM model due to Cheriyan and Ramabhadran's and Mckay's bivariate gamma model).
1
References
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TL;DR: For estimating the common mean of k (greater than or equal to 2) normal populations with order restricted unknown variances, using the theory of isotonic regression, this paper proposed a simple estimator which is better than the usual Graybill-deal estimator in terms of stochastic dominance and the Pitman measure of closeness.
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On order restricted location parameters of two exponential distributions
Nabendu Pal,Debashis Kushary +1 more
TL;DR: In this paper, independent randam aamples are drawn from tuo populations following exponential diatributions with unkncwn looation and aaale parametera.
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Pitman nearness comparisons of estimates of two ordered normal means
TL;DR: In this article, the authors compared the maximum likelihood estimates of ordered means of two normal distributions having common variance with respect to Pitman Nearness criterion, and showed that the latter has smaller mean square error than the former.
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Simultaneous estimation of two ordered exponential parameters
G. Vijayasree,Harshinder Singh +1 more
TL;DR: In this paper, a mixed estimator of (λ 1, λ 2 ), λ 1 ≤ λ2 has been shown to outperform the usual estimator when the loss function is the sum of squared errors and a class of estimators admissible in the class of mixed estimators are found.
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Estimation for Monotone Parameter Sequences: The Discrete Case
TL;DR: Theorem 4.3 as mentioned in this paper states that the minimax value of the problem of estimating ordered translation parameters is the same for all k = 1,2, \cdots, and the Corollary to Theorem 3.3 is that the existing admissible minimax estimators are undesirable.