Citations (28)
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Read on this site (10)
Navid Feroze & Muhammad Aslam. (2019) Approximate Bayesian analysis of doubly censored samples from mixture of two Weibull distributions. Communications in Statistics - Theory and Methods 48:11, pages 2862-2878.
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Tabassum Naz Sindhu, Zawar Hussain & Muhammad Aslam. (2019) Parameter and reliability estimation of inverted Maxwell mixture model. Journal of Statistics and Management Systems 22:3, pages 459-493.
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Tabassum Naz Sindhu, Navid Feroze & Muhammad Aslam. (2017) A class of improved informative priors for bayesian analysis of two-component mixture of failure time distributions from doubly censored data. Journal of Statistics and Management Systems 20:5, pages 871-900.
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Tatsuya Kubota & Takeshi Kurosawa. (2017) Bayesian prediction of unobserved values for Type-II censored data. Journal of Applied Statistics 44:7, pages 1165-1180.
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I.E. Okorie, A.C. Akpanta, J. Ohakwe & D.C. Chikezie. (2017) The modified Power function distribution. Cogent Mathematics 4:1.
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Mirza Naveed Shahzad & Zahid Asghar. (2016) Transmuted Power Function Distribution : A more flexible Distribution. Journal of Statistics and Management Systems 19:4, pages 519-539.
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Tabassum Naz Sindhu, Muhammad Aslam & Zawar Hussain. (2016) A simulation study of parameters for the censored shifted Gompertz mixture distribution: A Bayesian approach. Journal of Statistics and Management Systems 19:3, pages 423-450.
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Jafer Rahman & Muhammad Aslam. (2014) Interval Prediction of Future Order Statistics in Two-Component Mixture Inverse Lomax Model: A Bayesian Approach. American Journal of Mathematical and Management Sciences 33:3, pages 216-227.
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Mohsin Ali Kazmi, Muhammad Aslam, Sajid Ali & Nasir Abbas. (2013) Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets. Journal of Applied Statistics 40:8, pages 1639-1658.
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Farzana Noor & Muhammad Aslam. (2013) Bayesian inference of the inverse Weibull mixture distribution using type-I censoring. Journal of Applied Statistics 40:5, pages 1076-1089.
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Articles from other publishers (18)
Nadeem Akhtar, Sajjad Ahmad Khan, Emad A. A. Ismail, Fuad A. Awwad, Akbar Ali Khan, Taza Gul & Haifa Alqahtani. (2024) Analyzing quantitative performance: Bayesian estimation of 3-component mixture geometric distributions based on Kumaraswamy prior. Statistical Papers.
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Tahir Abbas, Muhammad Tahir, Muhammad Abid, Samavia Munir & Sajid Ali. (2024) The 3-component mixture of power distributions under Bayesian paradigm with application of life span of fatigue fracture. Scientific Reports 14:1.
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Emmanuel Afuecheta, Mujahida Sayyed, Idika E. Okorie & Saralees Nadarajah. (2023) A compound exponential distribution with application to control charts. Journal of Computational and Applied Mathematics 417, pages 114598.
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Muntazir Mehdi, Muhammad Aslam & Navid Feroze. (2022) Bayesian Estimation of Transmuted Lomax Mixture Model with an Application to Type-I Censored Windshield Data. Pakistan Journal of Statistics and Operation Research, pages 1027-1048.
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Yang Liu, Majid Khan, Syed Masroor Anwar, Zahid Rasheed & Navid Feroze. (2022) Stress-Strength Reliability and Randomly Censored Model of Two-Parameter Power Function Distribution. Mathematical Problems in Engineering 2022, pages 1-12.
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Navid FEROZE & Muhammad ASLAM. (2021) Comparison of improved class of priors for the analysis of the Burr type VII model under doubly censored samples. Hacettepe Journal of Mathematics and Statistics 50:5, pages 1509-1533.
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Muhammad Aslam & Navid Feroze. (2020) Bayesian Analysis of Two-Component Mixture of Weibull Distributions Using Approximation Techniques. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 91:3, pages 523-536.
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Maryam Khalid, Muhammad Aslam & Tabassum Naz Sindhu. (2020) Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling. Alexandria Engineering Journal 59:4, pages 2753-2763.
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E. I. Abdul-Sathar & R. B. Athira Krishnan. (2019) E-Bayesian and Hierarchical Bayesian Estimation for the Shape Parameter and Reversed Hazard Rate of Power Function Distribution Under Different Loss Functions. Journal of the Indian Society for Probability and Statistics 20:2, pages 227-253.
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Navid Feroze & Muhammad Aslam. (2018) Three-Component Mixture of Rayleigh Model Under Doubly Censored Samples: A Bayesian Look. Communications in Mathematics and Statistics 7:4, pages 417-443.
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Tabasam Sultana, Muhammad Aslam & Javid Shabbir. (2017) Bayesian Estimation of 3-Component Mixture of the Inverse Weibull Distributions. Iranian Journal of Science and Technology, Transactions A: Science 43:1, pages 255-263.
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Muhammad Saleem, Kashif Sharif & Aliya Fahmi. (2018) An algebraic aspect of Pareto mixture parameter estimation using censored sample: A Bayesian approach. Journal of Integrative Neuroscience 17:3-4, pages 463-477.
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Jafer Rahman & Muhammad Aslam. (2014) On estimation of two-component mixture inverse Lomax model via Bayesian approach. International Journal of System Assurance Engineering and Management 8:S1, pages 99-109.
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M. Tahir, M. Aslam & Z. Hussain. (2016) Bayesian estimation of finite3-component mixture of Burr Type-XII distributions assuming Type-I right censoring scheme. Alexandria Engineering Journal 55:4, pages 3277-3295.
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Tabassum Naz Sindhu, Navid Feroze & Muhammad Aslam. (2016) Doubly Censored Data from Two-Component Mixture of Inverse Weibull Distributions: Theory and Applications. Journal of Modern Applied Statistical Methods 15:2, pages 322-349.
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Muhammad Shakeel, Muhammad Ahsan ul Haq, Ijaz Hussain, Alaa Mohamd Abdulhamid & Muhammad Faisal. (2016) Comparison of Two New Robust Parameter Estimation Methods for the Power Function Distribution. PLOS ONE 11:8, pages e0160692.
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Muhammad Tahir & Muhammad Aslam. (2015) Bayesian Analysis of the 3-Component Mixture of Exponential Distribution Assuming the Non-Informative Priors. Revista Colombiana de Estadística 38:2, pages 431-452.
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Muhammad Aslam, Muhammad Tahir, Zawar Hussain & Bander Al-Zahrani. (2015) A 3-Component Mixture of Rayleigh Distributions: Properties and Estimation in Bayesian Framework. PLOS ONE 10:5, pages e0126183.
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