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Journal articles on the topic 'Bayesian parameter estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Kramer, S. C., and H. W. Sorenson. "Bayesian parameter estimation." IEEE Transactions on Automatic Control 33, no. 2 (1988): 217–22. http://dx.doi.org/10.1109/9.395.

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Mudasir, Sofi, Ajaz Bhat, Sheikh Ahmad, et al. "A dual approach to parameter estimation classical vs. Bayesian methods in power Rayleigh modelling." Thermal Science 28, no. 6 Part B (2024): 4877–94. https://doi.org/10.2298/tsci2406877m.

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In this article, we investigated the problem of estimating the parameters of power Rayleigh distribution using a range of classical and Bayesian estimate strategies. For applied statisticians and reliability engineers, parameter estimation provides a guide for choosing the best method of estimating the model parameters. Six frequentist estimation methods, including maximum likelihood estimation, Cramer-von Mises estimation, Anderson-Darling estimation, least square estimation, weighted least square estimation, and maximum product of spacing estimation, were taken into consideration when estima

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Li, Mingyao, and Juanping Zhu. "Bayesian Adaptive Estimation with Theoretical Bound: An Exploration-Exploitation Approach." Computational Intelligence and Neuroscience 2022 (December 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/1143056.

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This paper investigates the theoretical bound to reduce the parameter uncertainty in Bayesian adaptive estimation for psychometric functions and proposes an exploration-exploitation (E-E) approach to improve the computation efficiency for parameter estimations. When the experimental trial goes on, the uncertainty of the parameters decreases dramatically and the space between the maximal mutual information and the theoretical bound gets narrower, so the advantage of classical Bayesian adaptive estimation algorithm diminishes. This approach tries to trade off the exploration (parameter posterior

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Mohammed Alomari, Huda. "Bayes Estimations for Parameter of the Poisson distribution with Progressive Schemes." Academic Journal of Applied Mathematical Sciences, no. 102 (October 9, 2024): 14–23. https://doi.org/10.32861/ajams.10.2.14.23.

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This study introduces maximum likelihood and Bayesian approaches to Poisson parameter estimation using posterior distribution. I discuss three types of loss functions: the asymmetric linear exponential loss function, non-linear exponential loss function, and squared error loss function. Their performance is compared with the maximum likelihood estimator using mean squared error (MSE) as the test criterion. The proposed method with the classical estimator (maximum likelihood estimator) is better than that with the non-classical estimators for point estimation with different sample sizes. Maximu

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Al-Bossly, Afrah. "E-Bayesian and Bayesian Estimation for the Lomax Distribution under Weighted Composite LINEX Loss Function." Computational Intelligence and Neuroscience 2021 (December 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/2101972.

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The main contribution of this work is the development of a compound LINEX loss function (CLLF) to estimate the shape parameter of the Lomax distribution (LD). The weights are merged into the CLLF to generate a new loss function called the weighted compound LINEX loss function (WCLLF). Then, the WCLLF is used to estimate the LD shape parameter through Bayesian and expected Bayesian (E-Bayesian) estimation. Subsequently, we discuss six different types of loss functions, including square error loss function (SELF), LINEX loss function (LLF), asymmetric loss function (ASLF), entropy loss function

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Gao, Huiqing, Zhanshou Chen, and Fuxiao Li. "Linear Bayesian Estimation of Misrecorded Poisson Distribution." Entropy 26, no. 1 (2024): 62. http://dx.doi.org/10.3390/e26010062.

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Parameter estimation is an important component of statistical inference, and how to improve the accuracy of parameter estimation is a key issue in research. This paper proposes a linear Bayesian estimation for estimating parameters in a misrecorded Poisson distribution. The linear Bayesian estimation method not only adopts prior information but also avoids the cumbersome calculation of posterior expectations. On the premise of ensuring the accuracy and stability of computational results, we derived the explicit solution of the linear Bayesian estimation. Its superiority was verified through nu

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Guure, Chris Bambey, Noor Akma Ibrahim, and Al Omari Mohammed Ahmed. "Bayesian Estimation of Two-Parameter Weibull Distribution Using Extension of Jeffreys' Prior Information with Three Loss Functions." Mathematical Problems in Engineering 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/589640.

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The Weibull distribution has been observed as one of the most useful distribution, for modelling and analysing lifetime data in engineering, biology, and others. Studies have been done vigorously in the literature to determine the best method in estimating its parameters. Recently, much attention has been given to the Bayesian estimation approach for parameters estimation which is in contention with other estimation methods. In this paper, we examine the performance of maximum likelihood estimator and Bayesian estimator using extension of Jeffreys prior information with three loss functions, n

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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN." PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, no. 2 (2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.

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In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of glo

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Supharakonsakun, Yadpirun. "Bayesian Approaches for Poisson Distribution Parameter Estimation." Emerging Science Journal 5, no. 5 (2021): 755–74. http://dx.doi.org/10.28991/esj-2021-01310.

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The Bayesian approach, a non-classical estimation technique, is very widely used in statistical inference for real world situations. The parameter is considered to be a random variable, and knowledge of the prior distribution is used to update the parameter estimation. Herein, two Bayesian approaches for Poisson parameter estimation by deriving the posterior distribution under the squared error loss or quadratic loss functions are proposed. Their performances were compared with frequentist (maximum likelihood estimator) and Empirical Bayes approaches through Monte Carlo simulations. The mean s

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10

Alduais, Fuad. "Comparison of classical and Bayesian estimators to estimate the parameters in Weibull distribution under weighted general entropy loss function." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 3 (2021): 57–62. http://dx.doi.org/10.21833/ijaas.2021.03.008.

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In this work, we have developed a General Entropy loss function (GE) to estimate parameters of Weibull distribution (WD) based on complete data when both shape and scale parameters are unknown. The development is done by merging weight into GE to produce a new loss function called the weighted General Entropy loss function (WGE). Then, we utilized WGE to derive the parameters of the WD. After, we compared the performance of the developed estimation in this work with the Bayesian estimator using the GE loss function. Bayesian estimator using square error (SE) loss function, Ordinary Least Squar

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Durban, J. W., D. A. Elston, X. Lambin, and P. M. Thompson. "A role for Bayesian inference in cetacean population assessment." J. Cetacean Res. Manage. 2, no. 2 (1999): 117–23. http://dx.doi.org/10.47536/jcrm.v2i2.495.

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Decisions concerning the management and conservation of cetacean populations depend upon knowledge of population parameters, which generally must be estimated from sample data using statistical models. However, data from the cetacean populations are often sparse, and resultant parameter estimates can be uncertain and difficult to obtain. This review uses examples from published work to highlight the utility of the Bayesian statistical paradigm as a suitable estimation framework in these situations. By evaluating the probability of obtaining the available data, given a specified estimator model

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Pandey, Himanshu. "Bayesian Estimation of the Shape Parameter of Exponentiated lomax Distribution." Research International Journal of Physics and Mathematical Sciences 01, no. 01 (2021): 001–4. http://dx.doi.org/10.37179/rijpms.000001.

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In this paper, the exponentiated Lomax distribution is considered for Bayesian analysis. The Bayes estimators of the shape parameter have been obtained under squared error, precautionary, entropy, K-loss, and Al-Bayyati’s loss functions by using quasi and gamma priors.

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Journal, Baghdad Science. "Bayes and Non-Bayes Estimation Methods for the Parameter of Maxwell-Boltzmann Distribution." Baghdad Science Journal 14, no. 4 (2017): 808–12. http://dx.doi.org/10.21123/bsj.14.4.808-812.

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In this paper, point estimation for parameter ? of Maxwell-Boltzmann distribution has been investigated by using simulation technique, to estimate the parameter by two sections methods; the first section includes Non-Bayesian estimation methods, such as (Maximum Likelihood estimator method, and Moment estimator method), while the second section includes standard Bayesian estimation method, using two different priors (Inverse Chi-Square and Jeffrey) such as (standard Bayes estimator, and Bayes estimator based on Jeffrey's prior). Comparisons among these methods were made by employing mean squar

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14

Liu, Kaiwei, and Yuxuan Zhang. "The E-Bayesian Estimation for Lomax Distribution Based on Generalized Type-I Hybrid Censoring Scheme." Mathematical Problems in Engineering 2021 (May 19, 2021): 1–19. http://dx.doi.org/10.1155/2021/5570320.

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This article studies the E-Bayesian estimation of the unknown parameter of Lomax distribution based on generalized Type-I hybrid censoring. Under square error loss and LINEX loss functions, we get the E-Bayesian estimation and compare its effectiveness with Bayesian estimation. To measure the error of E-Bayesian estimation, the expectation of mean square error (E-MSE) is introduced. With Markov chain Monte Carlo technology, E-Bayesian estimations are computed. Metropolis–Hastings algorithm is applied within the process. Similarly, the credible interval for the parameter is calculated. Then, we

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Liu, Qing, David Pitt, Xibin Zhang, and Xueyuan Wu. "A Bayesian Approach to Parameter Estimation for Kernel Density Estimation via Transformations." Annals of Actuarial Science 5, no. 2 (2011): 181–93. http://dx.doi.org/10.1017/s1748499511000030.

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AbstractIn this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibits a high-level of skewness in the marginal empirical distributions. Therefore, the kernel density estimator based on original data does not perform well. However, the density of the original data can be estimated through estimating the density of the transformed data using kernels. It is well known that the performance

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16

Fries, R. H., and N. K. Cooperrider. "Bayesian Estimation of Transit Rail Vehicle Parameters." Journal of Dynamic Systems, Measurement, and Control 107, no. 2 (1985): 151–58. http://dx.doi.org/10.1115/1.3149687.

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Bayesian parameter estimation is a powerful and versatile method with good convergence properties. This paper describes the use of the Bayesian method to estimate the parameters of a transit rail vehicle. The paper also addresses several issues of general interest in estimation work: cost function formulation, signal processing methods, parameter linking, and parameter estimability.

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Ming, Han. "Estimation of Failure Probability and its Applications in Reliability Data Analysis." Advanced Materials Research 118-120 (June 2010): 601–5. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.601.

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Evaluation method of reliability parameter estimation needs to be improved effectively with the advance of science and technology. This paper develops a new method of parameter estimation, which is named E-Bayesian estimation method. In the case one hyper-parameter, the definition of E-Bayesian estimation of the failure probability is provided, moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation, and the property of E-Bayesian estimation of the failure probability are also provided. Finally, calculation on practical problems shows that the provided method is fe

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18

Nassar, Mazen, Refah Alotaibi, Hassan Okasha, and Liang Wang. "Bayesian Estimation Using Expected LINEX Loss Function: A Novel Approach with Applications." Mathematics 10, no. 3 (2022): 436. http://dx.doi.org/10.3390/math10030436.

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The loss function plays an important role in Bayesian analysis and decision theory. In this paper, a new Bayesian approach is introduced for parameter estimation under the asymmetric linear-exponential (LINEX) loss function. In order to provide a robust estimation and avoid making subjective choices, the proposed method assumes that the parameter of the LINEX loss function has a probability distribution. The Bayesian estimator is then obtained by taking the expectation of the common LINEX-based Bayesian estimator over the probability distribution. This alternative proposed method is applied to

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19

Banerjee, Proloy, and Babulal Seal. "Partial Bayes Estimation of Two Parameter Gamma Distribution Under Non-Informative Prior." Statistics, Optimization & Information Computing 10, no. 4 (2021): 1110–25. http://dx.doi.org/10.19139/soic-2310-5070-1110.

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In Bayesian analysis, empirical and hierarchical methods are two main approaches for the estimation of the parameter(s) involved in the prior distribution of one parameter. But in the multi-parameter model, e.g., Gamma(α, p), where both the parameters are unknown, idea of the ‘Partial Bayes (PB) Estimation’ is introduced. When we do no have proper belief regarding the joint parameters of the distribution of the variable and when we are estimating one parameter in presence of others, such method may be used. Partial Bayes estimation of the scale parameter p is done by putting the estimate of th

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Tsutakawa, Robert K., and Michael J. Soltys. "Approximation for Bayesian Ability Estimation." Journal of Educational Statistics 13, no. 2 (1988): 117–30. http://dx.doi.org/10.3102/10769986013002117.

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An approximation is proposed for the posterior mean and standard deviation of the ability parameter in an item response model. The procedure assumes that approximations to the posterior mean and covariance matrix of item parameters are available. It is based on the posterior mean of a Taylor series approximation to the posterior mean conditional on the item parameters. The method is illustrated for the two-parameter logistic model using data from an ACT math test with 39 items. A numerical comparison with the empirical Bayes method using n = 400 examinees shows that the point estimates are ver

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Yanuar, Ferra, Sintya Wulandari, Yudiantri Asdi, Aidinil Zetra, and Haripamyu. "Modeling of Human Development Index Using Bayesian Spatial Autoregressive Approach." Science and Technology Indonesia 10, no. 1 (2025): 72–79. https://doi.org/10.26554/sti.2025.10.1.72-79.

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Spatial regression analysis is a technique employed to examine the relationship between independent and dependent variables in datasets that exhibit regional neighborhood influences or spatial effects. When a spatial effect exists for the independent variable, the Spatial Autoregressive (SAR) regression can be utilized. The Maximum Likelihood Estimation (MLE) is a commonly used parameter estimator for SAR. However, due to the limitations of MLE, the Bayesian method provides an alternative approach for parameter estimation. This study compares the results of SAR estimations using both MLE and B

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Wu, Shu-Fei. "Bayesian Interval Estimation for the Two-Parameter Exponential Distribution Based on the Right Type II Censored Sample." Symmetry 14, no. 2 (2022): 352. http://dx.doi.org/10.3390/sym14020352.

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The Bayesian interval estimation of the scale parameter for two-parameter exponential distribution is proposed based on the right type II censored sample. Under this type of censoring, two methods of Bayesian joint confidence region of the two parameters are also proposed. The simulation results show that the Bayesian method has a higher coverage probability than the existing method, so the Bayesian method is recommended for use. This research is related to the topic of asymmetrical probability distributions and applications across disciplines. The predictive interval of the future observation

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23

Wei, Cheng Dong, Fu Wang, and Huan Qi Wei. "Bayesian Estimate of Exponential Parameter with Missing Data." Applied Mechanics and Materials 321-324 (June 2013): 904–8. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.904.

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We discuss the empirical Bayesian estimation and the noninformative prior Bayesian estimation of Exponential parameter in the missing data occasion. By setting different prior distributions, we get different bayesian risks and compare the numerical simulation results through the MATLAB programming.

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Eldemery, E. M., A. M. Abd-Elfattah, K. M. Mahfouz, and Mohammed M. El Genidy. "Bayesian and E-Bayesian Estimation for the Generalized Rayleigh Distribution under Different Forms of Loss Functions with Real Data Application." Journal of Mathematics 2023 (August 31, 2023): 1–25. http://dx.doi.org/10.1155/2023/5454851.

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This paper investigates the estimation of an unknown shape parameter of the generalized Rayleigh distribution using Bayesian and expected Bayesian estimation techniques based on type-II censoring data. Subsequently, these estimators are obtained using four different loss functions: the linear exponential loss function, the weighted linear exponential loss function, the compound linear exponential loss function, and the weighted compound linear exponential loss function. The weighted compound linear exponential loss function is a novel suggested loss function generated by combining weights with

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25

Mohammed Ahmed, Dr Al Omari. "Bayesian Methods and Maximum Likelihood Estimations of Exponential Censored Time Distribution with Cure Fraction." Academic Journal of Applied Mathematical Sciences, no. 72 (March 6, 2021): 106–12. http://dx.doi.org/10.32861/ajams.72.106.112.

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This paper is focused on estimating the parameter of Exponential distribution under right-censored data with cure fraction. The maximum likelihood estimation and Bayesian approach were used. The Bayesian method is implemented using gamma, Jeffreys, and extension of Jeffreys priors with two loss functions, which are; squared error loss function and Linear Exponential Loss Function (LINEX). The methods of the Bayesian approach are compared to maximum likelihood counterparts and the comparisons are made with respect to the Mean Square Error (MSE) to determine the best for estimating the parameter

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Wang, Jianhua, Dan Li, and Difang Chen. "E Bayesian Estimation and Hierarchical Bayesian Estimation of the System Reliability Parameter." Systems Engineering Procedia 3 (2012): 282–89. http://dx.doi.org/10.1016/j.sepro.2011.11.031.

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Ashok, Abbarapu, and Nadiminti Nagamani. "Adaptive estimation: Fuzzy data-driven gamma distribution via Bayesian and maximum likelihood approaches." AIMS Mathematics 10, no. 1 (2025): 438–59. https://doi.org/10.3934/math.2025021.

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<p>Integrating fuzzy concepts into statistical estimation offers considerable advantages by enhancing both the accuracy and reliability of parameter estimations, irrespective of the sample size and technique used. This study specifically examined the improvement of parameter estimation accuracy when dealing with fuzzy data, with a focus on the gamma distribution. We explored and evaluated a variety of estimation techniques for determining the scale parameter $ \eta $ and shape parameter $ \rho $ of the gamma distribution, employing both maximum likelihood (ML) and Bayesian methods. In th

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Thomson, A. H., and B. Whiting. "Bayesian Parameter Estimation and Population Pharmacokinetics." Clinical Pharmacokinetics 22, no. 6 (1992): 447–67. http://dx.doi.org/10.2165/00003088-199222060-00004.

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29

Gruber, Marvin H. J., and G. Larry Bretthorst. "Bayesian Spectrum Analysis and Parameter Estimation." Technometrics 32, no. 2 (1990): 226. http://dx.doi.org/10.2307/1268874.

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Tsay, Ruey S., and G. Larry Bretthorst. "Bayesian Spectrum Analysis and Parameter Estimation." Journal of the American Statistical Association 85, no. 409 (1990): 258. http://dx.doi.org/10.2307/2289561.

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31

Burr, Thomas L. "Bayesian Inference: Parameter Estimation and Decisions." Technometrics 46, no. 2 (2004): 250–51. http://dx.doi.org/10.1198/tech.2004.s793.

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Dieckman, Eric A., and Ning Xiang. "Bayesian parameter estimation of porous materials." Journal of the Acoustical Society of America 126, no. 4 (2009): 2235. http://dx.doi.org/10.1121/1.3249182.

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Guber, Marvin H. J. "Bayesian Spectrum Analysis and Parameter Estimation." Technometrics 32, no. 2 (1990): 226. http://dx.doi.org/10.1080/00401706.1990.10484646.

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Jin, Bangti. "Fast Bayesian approach for parameter estimation." International Journal for Numerical Methods in Engineering 76, no. 2 (2008): 230–52. http://dx.doi.org/10.1002/nme.2319.

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Hasaballah, Mustafa M., Oluwafemi Samson Balogun, and Mahmoud E. Bakr. "Investigation of Exponential Distribution Utilizing Randomly Censored Data under Balanced Loss Functions and Its Application to Clinical Data." Symmetry 15, no. 10 (2023): 1854. http://dx.doi.org/10.3390/sym15101854.

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In this research, random censoring is employed as a methodology for parameter estimation within the context of an exponential distribution. These parameter estimations are conducted using both the Bayesian and maximum likelihood approaches. In the Bayesian framework, Lindley’s approximation method is applied to derive estimates, which are subsequently assessed under three distinct balanced loss functions. To gauge the efficacy of different estimation techniques, simulation-based investigations are conducted. Additionally, a real-world data analysis is executed to illustrate the practical appli

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Li, Yan, Luca Pezzè, Manuel Gessner, Zhihong Ren, Weidong Li, and Augusto Smerzi. "Frequentist and Bayesian Quantum Phase Estimation." Entropy 20, no. 9 (2018): 628. http://dx.doi.org/10.3390/e20090628.

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Frequentist and Bayesian phase estimation strategies lead to conceptually different results on the state of knowledge about the true value of an unknown parameter. We compare the two frameworks and their sensitivity bounds to the estimation of an interferometric phase shift limited by quantum noise, considering both the cases of a fixed and a fluctuating parameter. We point out that frequentist precision bounds, such as the Cramér–Rao bound, for instance, do not apply to Bayesian strategies and vice versa. In particular, we show that the Bayesian variance can overcome the frequentist Cramér–Ra

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Adepoju, Adedayo A., Oluwayemisi O, Alaba, and P. Ogundunmadetayo. "Bayesian estimation of simultaneous equation model with lagged endogenous variables and first order serially correlated errors." Global Journal of Pure and Applied Sciences 24, no. 2 (2018): 235–44. http://dx.doi.org/10.4314/gjpas.v24i2.14.

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Most simultaneous equation models involve the inclusion of lagged endogenous and/or exogenous variables and sometimes it may be misleading to assume that the errors are normally distributed when in reality they exhibit functional formsthat are not normal especially in practical situations. The classical methods of estimating parameters of simultaneous equation models are usually affected by the presence of autocorrelation among the error terms. Unfortunately, in practice the form of correlation between the pairs of the random deviates is unknown.In this paper classical and Bayesian methods for

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Xiang, Ning, and Christopher Landschoot. "Bayesian Inference for Acoustic Direction of Arrival Analysis Using Spherical Harmonics." Entropy 21, no. 6 (2019): 579. http://dx.doi.org/10.3390/e21060579.

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This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming. When estimating the DoA, the acoustic signals may potentially contain one or multiple simultaneous sources. Using two levels of Bayesian inference, this work begins by estimating the correct number of sources via the higher level of inference, Bayesian model selection. It is followed by estimating the directional information of each source via the lower

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Vinaitheerthan, Renganathan. "Maximum Likelihood Estimation and Likelihood Ratio test revisited." www.vinaitheerthan.com 1, no. 1 (2017): 1–4. https://doi.org/10.5281/zenodo.579557.

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Maximum likelihood Estimation is an important aspect of frequentist approach which was introduced by RA Fisher [1]. Maximum Likelihood estimation method helps us to find the estimator for the unknown population parameter. There are other methods of estimation also available such as Least Square Estimation and Bayesian Estimation methods but Maximum Likelihood Estimation is the widely used method to estimate the parameters. This paper provides an overview of Maximum Likelihood Method with example to calculate a Maximum Likelihood Estimator from a sample data set.

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Asteris, Georgios, and Sahotra Sarkar. "Bayesian Procedures for the Estimation of Mutation Rates from Fluctuation Experiments." Genetics 142, no. 1 (1996): 313–26. http://dx.doi.org/10.1093/genetics/142.1.313.

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Bayesian procedures are developed for estimating mutation rates from fluctuation experiments. Three Bayesian point estimators are compared with four traditional ones using the results of 10,000 simulated experiments. The Bayesian estimators were found to be at least as efficient as the best of the previously known estimators. The best Bayesian estimator is one that uses (1/m 2) as the prior probability density function and a quadratic loss function. The advantage of using these estimators is most pronounced when the number of fluctuation test tubes is small. Bayesian estimation allows the inco

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Chaturvedi, Ankita, Sanjay Kumar Singh, and Umesh Singh. "Statistical Inferences of Type-II Progressively Hybrid Censored Fuzzy Data with Rayleigh Distribution." Austrian Journal of Statistics 47, no. 3 (2018): 40–62. http://dx.doi.org/10.17713/ajs.v47i3.752.

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This article presents the procedures for the estimation of the parameter of Rayleighdistribution based on Type-II progressive hybrid censored fuzzy lifetime data. Classicalas well as the Bayesian procedures for the estimation of unknown model parameters has been developed. The estimators obtained here are Maximum likelihood (ML) estimator, Method of moments (MM) estimator, Computational approach (CA) estimator and Bayes estimator. Highest posterior density (HPD) credible intervals of the unknown parameter are obtained by using Markov Chain Monte Carlo (MCMC) technique. For numerical illustrati

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Usen, John Effiong, Emmanuel Emmanuel Asuk, and Godswill Itobo Egede. "A Bayesian Estimation Procedure for One-Parameter Exponential Survival Distributions Facilitated by an Inverted Gamma Prior." International Journal of Research and Innovation in Applied Science X, no. V (2025): 881–905. https://doi.org/10.51584/ijrias.2025.100500079.

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A Bayesian estimation procedure for one-parameter exponential survival distributions facilitated by an inverted gamma prior was performed in this study using data obtained from data obtained from the University of Calabar Teaching Hospital (UCTH). The exponential survival distributions are just one amongst a number of distributions adopted for tackling problems in survival analysis, and may occur either in one parameter or two parameters under uncensored or censored conditions. The review of literature exposed the absence of studies addressing the need for an alternative procedure that careful

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Aniyan, Anitta Susan, and Dais George. "BAYESIAN MODELING OF FINANCIAL DATA USING ESSCHER TRANSFORMED LAPLACE DISTRIBUTION IN STAN." Advances and Applications in Statistics 92, no. 7 (2025): 1031–56. https://doi.org/10.17654/0972361725046.

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This study investigates the application of Bayesian methods for estimating parameter of the Esscher transformed Laplace distribution, renowned for its ability to capture the asymmetry and heavy tails commonly observed in financial data. This paper also focuses on the Bayesian estimation of stress strength parameter using both squared error and LINEX loss functions. A simulation study is conducted to compare the performance of the proposed Bayesian estimators for the unknown parameter and stress strength parameter with maximum likelihood estimator based on mean squared error. For the simulation

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Okasha, Hassan M., Heba S. Mohammed, and Yuhlong Lio. "E-Bayesian Estimation of Reliability Characteristics of a Weibull Distribution with Applications." Mathematics 9, no. 11 (2021): 1261. http://dx.doi.org/10.3390/math9111261.

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Given a progressively type-II censored sample, the E-Bayesian estimates, which are the expected Bayesian estimates over the joint prior distributions of the hyper-parameters in the gamma prior distribution of the unknown Weibull rate parameter, are developed for any given function of unknown rate parameter under the square error loss function. In order to study the impact from the selection of hyper-parameters for the prior, three different joint priors of the hyper-parameters are utilized to establish the theoretical properties of the E-Bayesian estimators for four functions of the rate param

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Ashour, Samir Kamel, and Mohamed Salem Abdelwahab Muiftah. "Bayesian Estimation of the Parameters of Discrete Weibull Type (I) Distribution." Journal of Modern Applied Statistical Methods 18, no. 2 (2020): 2–13. http://dx.doi.org/10.22237/jmasm/1604189160.

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Bayesian estimation of the continuous Weibull distribution parameters was studied by Ahmad and Ahmad (2013) under the assumption of knowing the shape parameter. Bayesian estimates are considered here of the parameters of the discrete Weibull Type I [DW(I)] distribution and are obtained under two different assumptions: when the shape parameter is known, and when both parameters are independent random variables. A Mathcad program is performed to simulate data from the DW(I) distribution considering different values of the parameters and different sample sizes, and to obtain Bayesian parameter es

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Khan, Mohammad Golam Mostafa, and Mohammed Rafiuddin Ahmed. "Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions." Wind Energy Science 8, no. 8 (2023): 1277–98. http://dx.doi.org/10.5194/wes-8-1277-2023.

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Abstract. The two-parameter Weibull distribution has garnered much attention in the assessment of wind energy potential. The estimation of the shape and scale parameters of the distribution has brought forth a successful tool for the wind energy industry. However, it may be inappropriate to use the two-parameter Weibull distribution to assess energy at every location, especially at sites where low wind speeds are frequent, such as in tropical regions. In this work, a robust technique for wind resource assessment using a Bayesian approach for estimating Weibull parameters is first proposed. Sec

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Eraikhuemen, Innocent Boyle, Abraham Iorkaa Asongo, Adamu Abubakar Umar, and Isa Abubakar Ibrahim. "Estimation of a Shape Parameter of a Gompertz-lindley Distribution Using Bayesian and Maximum Likelihood Methods." Journal of Scientific Research and Reports 29, no. 10 (2023): 85–98. http://dx.doi.org/10.9734/jsrr/2023/v29i101800.

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The Gompertz-Lindley distribution is an extension of the Lindley distribution with three parameters. It was found to be more flexible for modeling real life events. The distribution contains two shape parameters and a scale parameter. Despite the necessity of parameter estimation theory in modeling, it has not been shown that a method of estimation method is better for any of these three parameters of the Gompertz-Lindley distribution. This paper identifies the best estimation method for the shape parameter of the Gompertz-Lindley distribution by deriving Bayesian estimators for the shape para

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Oh, Mi-Ra, Kyung-Sook Kim, Wan-Hyun Cho, and Young-Sook Son. "Bayesian Parameter Estimation of the Four-Parameter Gamma Distribution." Communications for Statistical Applications and Methods 14, no. 1 (2007): 255–66. http://dx.doi.org/10.5351/ckss.2007.14.1.255.

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Rasheed, Huda Abdullah, and Maryam N. Abd. "Bayesian Estimation for Two Parameters of Exponential Distribution under Different Loss Functions." Ibn AL-Haitham Journal For Pure and Applied Sciences 36, no. 2 (2023): 289–300. http://dx.doi.org/10.30526/36.2.2946.

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In this paper, two parameters for the Exponential distribution were estimated using theBayesian estimation method under three different loss functions: the Squared error loss function,the Precautionary loss function, and the Entropy loss function. The Exponential distribution priorand Gamma distribution have been assumed as the priors of the scale γ and location δ parametersrespectively. In Bayesian estimation, Maximum likelihood estimators have been used as the initialestimators, and the Tierney-Kadane approximation has been used effectively. Based on the MonteCarlosimulation method, those es

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Tang, Yuanyuan, Philip G. Jones, Liangrui Sun, Suzanne V. Arnold, and John A. Spertus. "Constraint approaches to the estimation of relative risk." Statistical Methods in Medical Research 27, no. 11 (2017): 3436–46. http://dx.doi.org/10.1177/0962280217702934.

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In medical and epidemiologic studies, relative risk is usually the parameter of interest. However, calculating relative risk using standard log-Binomial regression approach often encounters non-convergence. A modified Poisson regression, which uses robust variance, was proposed by Zou in 2004. Although the modified Poisson regression with sandwich variance estimator is valid for the estimation of relative risk, the predicted probability of the outcome may be greater than the natural boundary 1 for the unobserved but plausible covariate combinations. Moreover, the lower and upper bounds of conf

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