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Showing 4 results for Subject:
Esmaeil Shirazi,
Volume 14, Issue 1 (8-2020)
Abstract
In this paper, we consider an adaptive wavelet estimation for quantile density function based on block thresholding method and obtain it's convergence rate under L2 loss function over Besove function spaces. This work is an extension of results in Chesneau et. al. (2016) and shows that the block threshold estimator gets better convergence rate (Optimal) than the estimators proposed by Chesneau et. al. (2016). The performance of the proposed estimator is investigated with a simulation study.
Mahdi Teimouri,
Volume 14, Issue 1 (8-2020)
Abstract
The class of α-stable distributions incorporates both heavy tails and skewness and so are the most widely used class of distributions in several fields of study which incorporates both the skewness and heavy tails. Unfortunately, there is no closed-form expression for the density function of almost all of the members of this class, and so finding the maximum likelihood estimator for the parameters of this distribution is a challenging problem. In this paper, in order to tackle this issue, we propose some type of EM algorithm. The performance of the proposed EM algorithm is demonstrated via simulation and analyzing three sets of real data.
Abdol Saeed Toomaj,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, the entropy characteristics of the lifetime of coherent systems are investigated using the concept of system signature. The results are based on the assumption that the lifetime distribution of system components is independent and identically distributed. In particular, a formula for calculating the Tsallis entropy of a coherent system's lifetime is presented, which is used to compare systems with the same characteristics. Also, bounds for the lifetime Tsallis entropy of coherent systems are presented. These bounds are especially useful when the system has many components or a complex structure. Finally, a criterion for selecting the preferred system among coherent systems based on the relative Tsallis entropy is presented.
Dr Mojtaba Kashani, Dr Reza Ghasemi,
Volume 19, Issue 2 (4-2025)
Abstract
In statistical research, experimental designs are used to investigate the effect of control variables on output responses. These methods are based on the assumption of normal distribution of data and face fundamental challenges in dealing with outliers. The present study examines five different examples of experimental design methods to deal with this challenge: Huber, quadratic, substitution, ranking, and fuzzy regression robustness methods. By providing empirical evidence from real data on seedling growth and weld quality, it is shown that fuzzy can be used as an efficient alternative to conventional methods in the presence of outliers. It is shown that fuzzy not only outperforms the classical experimental design method in the presence of outliers, but also outperforms standard robustness methods in handling outliers.
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