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Akram Kohansal
Akram Kohansal
Adresse e-mail validée de SCI.ikiu.ac.ir
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On estimation of reliability in a multicomponent stress-strength model for a Kumaraswamy distribution based on progressively censored sample
A Kohansal
Statistical Papers 60, 2185-2224, 2019
822019
Bayesian and classical estimation of reliability in a multicomponent stress-strength model under adaptive hybrid progressive censored data
A Kohansal, S Shoaee
Statistical Papers 62 (1), 309-359, 2021
452021
Stress–strength parameter estimation based on type-II hybrid progressive censored samples for a Kumaraswamy distribution
A Kohansal, S Nadarajah
IEEE Transactions on Reliability 68 (4), 1296-1310, 2019
172019
Inference of R = P(Y < X) for two-parameter Rayleigh distribution based on progressively censored samples
A Kohansal, S Rezakhah
Statistics 53 (1), 81-100, 2019
152019
Estimation procedures for Kumaraswamy distribution parameters under adaptive type-II hybrid progressive censoring
A Kohansal, HS Bakouch
Communications in Statistics-Simulation and Computation, 1-20, 2019
112019
Parameter estimation of Type-II hybrid censored weighted exponential distribution
A Kohansal, S Rezakhah, E Khorram
Communications in Statistics-Simulation and Computation 44 (5), 1273-1299, 2015
112015
MMSE and maximum a posteriori estimators for speech enhancement in additive noise assuming a t ‐location‐scale clean speech prior
N Faraji, A Kohansal
IET Signal Processing 12 (4), 532-543, 2018
102018
Multi-component stress–strength parameter estimation of a non-identical-component strengths system under the adaptive hybrid progressive censoring samples
A Kohansal, AJ Fernández, CJ Pérez-González
Statistics 55 (4), 925-962, 2021
92021
Improved time-censored reliability test plans for k-out-of-n gamma systems
AJ Fernández, CJ Pérez-González, A Kohansal
Journal of computational and applied mathematics 361, 42-54, 2019
92019
Large Estimation of the stress-strength reliability of progressively censored inverted exponentiated Rayleigh distributions
A Kohansal
Journal of Applied Mathematics, Statistics and Informatics 13 (1), 49-76, 2017
92017
Bayesian and classical estimation of based on Burr type XII distribution under hybrid progressive censored samples
A Kohansal
Communications in Statistics-theory and Methods 49 (5), 1043-1081, 2020
82020
Optimal truncated repetitive lot inspection with defect rates
CJ Pérez-González, AJ Fernández, A Kohansal, A Asgharzadeh
Applied Mathematical Modelling 75, 223-235, 2019
82019
Efficient truncated repetitive lot inspection using Poisson defect counts and prior information
CJ Pérez-González, AJ Fernández, A Kohansal
European Journal of Operational Research 287 (3), 964-974, 2020
72020
Testing Exponentiality Based on R\'enyi Entropy With Progressively Type-II Censored Data
A Kohansal, S Rezakhah
arXiv preprint arXiv:1303.5536, 2013
72013
Inference on stress-strength model for a Kumaraswamy distribution based on hybrid progressive censored sample
A Kohansal
REVSTAT-Statistical Journal 20 (1), 51-83, 2022
62022
Multi-component reliability inference in modified Weibull extension distribution and progressive censoring scheme
A Kohansal, CJ Pérez-González, AJ Fernández
Bulletin of the Malaysian Mathematical Sciences Society 46 (2), 61, 2023
52023
Stress–strength parameter estimation based on Type-II progressive censored samples for a Weibull-half-logistic distribution
R Kazemi, A Kohansal
Bulletin of the Malaysian Mathematical Sciences Society 44, 2531-2566, 2021
52021
Fitting skew distributions to Iranian auto insurance claim data
R Kazemi, A Jalilian, A Kohansal
Applications and Applied Mathematics: An International Journal (AAM) 12 (2), 10, 2017
52017
Two new estimators of entropy for testing normality
A Kohansal, S Rezakhah
Communications in Statistics-Theory and Methods 45 (18), 5392-5411, 2016
42016
Inference on the stress-strength reliability of multi-component systems based on progressive first failure censored samples
A Kohansal, CJ Pérez-González, AJ Fernández
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2023
22023
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