A general lower bound of minimax risk for absolute-error loss

1997 ◽  
Vol 25 (4) ◽  
pp. 545-558 ◽  
Author(s):  
Jeesen Chen
Bernoulli ◽  
2017 ◽  
Vol 23 (4B) ◽  
pp. 3197-3212 ◽  
Author(s):  
Tatsuya Kubokawa ◽  
Éric Marchand ◽  
William E. Strawderman

Metrika ◽  
1993 ◽  
Vol 40 (1) ◽  
pp. 283-298 ◽  
Author(s):  
Wolfgang Bischoff ◽  
Werner Fieger

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2758
Author(s):  
Mustapha Muhammad ◽  
Rashad A. R. Bantan ◽  
Lixia Liu ◽  
Christophe Chesneau ◽  
Muhammad H. Tahir ◽  
...  

In this article, we introduce a new extended cosine family of distributions. Some important mathematical and statistical properties are studied, including asymptotic results, a quantile function, series representation of the cumulative distribution and probability density functions, moments, moments of residual life, reliability parameter, and order statistics. Three special members of the family are proposed and discussed, namely, the extended cosine Weibull, extended cosine power, and extended cosine generalized half-logistic distributions. Maximum likelihood, least-square, percentile, and Bayes methods are considered for parameter estimation. Simulation studies are used to assess these methods and show their satisfactory performance. The stress–strength reliability underlying the extended cosine Weibull distribution is discussed. In particular, the stress–strength reliability parameter is estimated via a Bayes method using gamma prior under the square error loss, absolute error loss, maximum a posteriori, general entropy loss, and linear exponential loss functions. In the end, three real applications of the findings are provided for illustration; one of them concerns stress–strength data analyzed by the extended cosine Weibull distribution.


2020 ◽  
Vol 68 (2) ◽  
pp. 445-458 ◽  
Author(s):  
Siyuan Du ◽  
Jiashu Zhang ◽  
Guangmin Hu

Author(s):  
Di Wang ◽  
Jinhui Xu

In this paper, we study the sparse covariance matrix estimation problem in the local differential privacy model, and give a non-trivial lower bound on the non-interactive private minimax risk in the metric of squared spectral norm. We show that the lower bound is actually tight, as it matches a previous upper bound. Our main technique for achieving this lower bound is a general framework, called General Private Assouad Lemma, which is a considerable generalization of the previous private Assouad lemma and can be used as a general method for bounding the private minimax risk of matrix-related estimation problems.


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