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Journal articles on the topic 'Bayesian estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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admin, admin. "On The Bayesian Estimation of Parameters of SQDM." Neutrosophic and Information Fusion 3, no. 1 (2024): 34–41. http://dx.doi.org/10.54216/nif.030105.

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This work is concerned with the problem of estimating parameters of spatial quadratic models by Bayesian technique (SQDM). This technique involves the prior information of the first and second moment of the parameters, where its estimation model is called the Bayesian quadratic unbiased estimator. The results of the estimation are taken in compared with the estimates of minimum norm quadratic unbiased estimators.

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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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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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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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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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Shadmehr, Reza, and David Z. D'Argenio. "A Neural Network for Nonlinear Bayesian Estimation in Drug Therapy." Neural Computation 2, no. 2 (1990): 216–25. http://dx.doi.org/10.1162/neco.1990.2.2.216.

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The feasibility of developing a neural network to perform nonlinear Bayesian estimation from sparse data is explored using an example from clinical pharmacology. The problem involves estimating parameters of a dynamic model describing the pharmacokinetics of the bronchodilator theophylline from limited plasma concentration measurements of the drug obtained in a patient. The estimation performance of a backpropagation trained network is compared to that of the maximum likelihood estimator as well as the maximum a posteriori probability estimator. In the example considered, the estimator predict

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Wang, Wei, and Wenhao Gui. "Estimation and Bayesian Prediction for the Generalized Exponential Distribution Under Type-II Censoring." Symmetry 17, no. 2 (2025): 222. https://doi.org/10.3390/sym17020222.

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This research focuses on the prediction and estimation problems for the generalized exponential distribution under Type-II censoring. Firstly, maximum likelihood estimations for the parameters of the generalized exponential distribution are computed using the EM algorithm. Additionally, confidence intervals derived from the Fisher information matrix are developed and analyzed alongside two bootstrap confidence intervals for comparison. Compared to classical maximum likelihood estimation, Bayesian inference proves to be highly effective in handling censored data. This study explores Bayesian in

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Itagaki, Hiroshi, Hiroo Asada, and Seiichi Itoh. "Bayesian Estimation." Journal of the Society of Naval Architects of Japan 1985, no. 157 (1985): 285–94. http://dx.doi.org/10.2534/jjasnaoe1968.1985.285.

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10

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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Ren, Haiping, Qin Gong, and Xue Hu. "Estimation of Entropy for Generalized Rayleigh Distribution under Progressively Type-II Censored Samples." Axioms 12, no. 8 (2023): 776. http://dx.doi.org/10.3390/axioms12080776.

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This paper investigates the problem of entropy estimation for the generalized Rayleigh distribution under progressively type-II censored samples. Based on progressively type-II censored samples, we first discuss the maximum likelihood estimation and interval estimation of Shannon entropy for the generalized Rayleigh distribution. Then, we explore the Bayesian estimation problem of entropy under three types of loss functions: K-loss function, weighted squared error loss function, and precautionary loss function. Due to the complexity of Bayesian estimation computation, we use the Lindley approx

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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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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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Hu, Xue, and Haiping Ren. "Statistical inference of the stress-strength reliability for inverse Weibull distribution under an adaptive progressive type-Ⅱ censored sample." AIMS Mathematics 8, no. 12 (2023): 28465–87. http://dx.doi.org/10.3934/math.20231457.

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<abstract><p>In this paper, we investigate classical and Bayesian estimation of stress-strength reliability $\delta = P(X &gt; Y)$ under an adaptive progressive type-Ⅱ censored sample. Assume that $X$ and $Y$ are independent random variables that follow inverse Weibull distribution with the same shape but different scale parameters. In classical estimation, the maximum likelihood estimator and asymptotic confidence interval are deduced. An approximate maximum likelihood estimator approach is used to obtain the explicit form. In Bayesian estimation, the Bayesian estimators are d

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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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Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (1997): 3–33. http://dx.doi.org/10.1142/s0218001497000020.

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A large number of image and spatial information processing problems involves the estimation of the intrinsic image information from observed images, for instance, image restoration, image registration, image partition, depth estimation, shape reconstruction and motion estimation. These are inverse problems and generally ill-posed. Such estimation problems can be readily formulated by Bayesian models which infer the desired image information from the measured data. Bayesian paradigms have played a very important role in spatial data analysis for over three decades and have found many successful

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Verma, Vivek, Dilip C. Nath, and S. N. Dwivedi. "Bayesian estimation of fertility rates under imperfect age reporting." Statistics in Transition new series 24, no. 2 (2023): 39–57. http://dx.doi.org/10.59170/stattrans-2023-019.

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This article outlines the application of the Bayesian method of parameter estimation to situations where the probability of age misreporting is high, leading to transfers of an individual from one age group to another. An essential requirement for Bayesian estimation is prior distribution, derived for both perfect and imperfect age reporting. As an alternative to the Bayesian methodology, a classical estimator based on the maximum likelihood principle has also been discussed. Here, the age misreporting probability matrix has been constructed using a performance indicator, which incorporates th

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18

Gustafson, Steven C., Christopher S. Costello, Eric C. Like, Scott J. Pierce, and Kiran N. Shenoy. "Bayesian Threshold Estimation." IEEE Transactions on Education 52, no. 3 (2009): 400–403. http://dx.doi.org/10.1109/te.2008.930092.

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19

Adams, W. J., and P. Mamassian. "Bayesian slant estimation." Journal of Vision 1, no. 3 (2010): 175. http://dx.doi.org/10.1167/1.3.175.

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20

Abdel-Mottaleb, Mohamed, and Azriel Rosenfeld. "Inexact Bayesian estimation." Pattern Recognition 25, no. 6 (1992): 641–46. http://dx.doi.org/10.1016/0031-3203(92)90080-3.

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21

Liechty, J. C. "Bayesian correlation estimation." Biometrika 91, no. 1 (2004): 1–14. http://dx.doi.org/10.1093/biomet/91.1.1.

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22

Christensen, Ronald. "Inconsistent Bayesian estimation." Bayesian Analysis 4, no. 4 (2009): 759–62. http://dx.doi.org/10.1214/09-ba428.

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23

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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24

Yuanxi, Yang. "Robust bayesian estimation." Bulletin Géodésique 65, no. 3 (1991): 145–50. http://dx.doi.org/10.1007/bf00806343.

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25

Han, Ming. "E-Bayesian Estimation and Hierarchical Bayesian Estimation of Failure Probability." Communications in Statistics - Theory and Methods 40, no. 18 (2011): 3303–14. http://dx.doi.org/10.1080/03610926.2010.498643.

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26

Han, Ming. "E-Bayesian estimation and hierarchical Bayesian estimation of failure rate." Applied Mathematical Modelling 33, no. 4 (2009): 1915–22. http://dx.doi.org/10.1016/j.apm.2008.03.019.

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27

Jia, Tianyi, Hongwei Liu, Penghui Wang, and Chang Gao. "Bayesian Direction of Arrival Estimation with Prior Knowledge from Target Tracker." Remote Sensing 15, no. 13 (2023): 3255. http://dx.doi.org/10.3390/rs15133255.

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The performance of traditional direction of arrival (DOA) estimation methods always deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This paper investigates the Bayesian DOA estimation problem aided by the prior knowledge from the target tracker. The Bayesian Cramér–Rao lower bounds (CRLB) and the expected CRLB are first derived to evaluate the theoretical performance of Bayesian DOA estimation. Based on the maximum a posterior (MAP) estimator in the Bayesian framework, two methods are proposed. One is a two-step grid search method for a single target DOA c

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28

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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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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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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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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Acquah, Henry De-Graft. "Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm." Journal of Social and Development Sciences 4, no. 4 (2013): 193–97. http://dx.doi.org/10.22610/jsds.v4i4.751.

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This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The Bayesian logistic regression estimation is compared with the classical logistic regression. Both the classical logistic regression and the Bayesian logistic regression suggest that higher per capita income is associated with free trade of countries. The results also show a reduction of standard errors associated with the coefficients obtained from the Bayesian analysis, thus bringing greater stability to the coefficients.

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Ozguven, Eren Erman, and Kaan Ozbay. "Nonparametric Bayesian Estimation of Freeway Capacity Distribution from Censored Observations." Transportation Research Record: Journal of the Transportation Research Board 2061, no. 1 (2008): 20–29. http://dx.doi.org/10.3141/2061-03.

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Previous studies have been made of the usefulness and effectiveness of survival analysis in transportation and traffic engineering studies with incomplete data in which the Kaplan–Meier estimate is proposed for determining traffic capacity distribution. However, well-known estimators like Kaplan–Meier and Nelson–Aalen have several disadvantages that make it difficult to obtain the traffic capacity distribution. First, neither estimator is defined for all values of traffic flows possible. That is, the maximum flow followed by a breakdown defines the final point of the estimated distribution cur

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Han, Ming. "Estimation of Failure Rate and its Applications in the Case of Zero-Failure Data." Advanced Materials Research 945-949 (June 2014): 1046–49. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1046.

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This paper introduces a new method, named E-Bayesian estimation method, to estimate failure rate in zero-failure data. The definition of E-Bayesian estimation of the failure rate is given, based on the definition, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation of the failure rate were provided, and properties of the E-Bayesian estimation, i. e. relations between E-Bayesian estimation and hierarchical Bayesian estimation, was discussed. Calculations were performed on practical problems, showing that the proposed new method is feasible and easy to operate.

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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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Stenling, Andreas, Andreas Ivarsson, Urban Johnson, and Magnus Lindwall. "Bayesian Structural Equation Modeling in Sport and Exercise Psychology." Journal of Sport and Exercise Psychology 37, no. 4 (2015): 410–20. http://dx.doi.org/10.1123/jsep.2014-0330.

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Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results in

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Wickramasinghe, Lahiru, Alexandre Leblanc, and Saman Muthukumarana. "Bayesian inference on sparse multinomial data using smoothed Dirichlet distribution with an application to COVID-19 data." Model Assisted Statistics and Applications 18, no. 3 (2023): 207–26. http://dx.doi.org/10.3233/mas-221411.

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We develop a Bayesian approach for estimating multinomial cell probabilities using a smoothed Dirichlet prior. The most important feature of the smoothed Dirichlet prior is that it forces the probabilities of neighboring cells to be closer to each other than under the standard Dirichlet prior. We propose a shrinkage-type estimator using this Bayesian approach to estimate multinomial cell probabilities. The proposed estimator allows us to borrow information across other multinomial populations and cell categories simultaneously to improve the estimation of cell probabilities, especially in a co

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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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Hashimoto, Noriaki, and Koji Konbune. "DIRECTIONAL SPECTRUM ESTIMATION FROM A BAYESIAN APPROACH." Coastal Engineering Proceedings 1, no. 21 (1988): 4. http://dx.doi.org/10.9753/icce.v21.4.

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A new directional spectral estimation method using a Bayesian approach is proposed. The proposed method is examined for numerical simulation data, and the validity of the method is discussed. Some examples of the directional spectra estimated from field observation data attained at an offshore oil rig utilizing seven wave probes are also shown in this report. The major conclusions of the report are : (1) The proposed method can be applied for more than four arbitrarily mixed instrument array measurements. (2) It has a higher resolution power than other existing methods for estimating direction

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Han, Ming. "E-Bayesian Estimation Method and its Applications in Reliability Engineering." Advanced Materials Research 199-200 (February 2011): 308–12. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.308.

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Evaluation method of reliability of industrial products needs to be improved effectively with the advance of science and technology. This paper introduces a new method, named E-Bayesian estimation method, to estimate failure probability in reliability engineering. The definition of E-Bayesian estimation of the failure probability is provided, moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation of the failure probability were provided, and properties of the E-Bayesian estimation, i.e. relations between E-Bayesian estimation and hierarchical Bayesian estimation,

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Han, Ming. "Estimation of Failure Probability for Bearing and its Applications in the Case of Zero-Failure Data." Advanced Materials Research 915-916 (April 2014): 318–22. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.318.

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This paper introduces a new method, named E-Bayesian estimation method, to estimate failure probability. In the case of zero-failure data, the definition of E-Bayesian estimation of 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. For the estimate failure probability, in the following sections we will see simple the E-Bayesian estimation method is method than hierarchical Bayesian estimation method. Finally, the calculated results of beari

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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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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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Körding, Konrad P., Shih-pi Ku, and Daniel M. Wolpert. "Bayesian Integration in Force Estimation." Journal of Neurophysiology 92, no. 5 (2004): 3161–65. http://dx.doi.org/10.1152/jn.00275.2004.

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When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, termed prior information. Bayesian integration defines the way in which these two sources of information should be combined to produce an optimal estimate. To investigate whether subjects use such a strat

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Kim, Su-Young, David Huh, Zhengyang Zhou, and Eun-Young Mun. "A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome." International Journal of Behavioral Development 44, no. 5 (2020): 447–57. http://dx.doi.org/10.1177/0165025419894730.

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Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a lack of research providing guidance on when Bayesian estimation may be preferable to ML or vice versa. This study compared the performance of Bayesia

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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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Neath, Andrew A., and Natalie Langenfeld. "A Note on the Comparison of the Bayesian and Frequentist Approaches to Estimation." Advances in Decision Sciences 2012 (October 22, 2012): 1–12. http://dx.doi.org/10.1155/2012/764254.

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Samaniego and Reneau presented a landmark study on the comparison of Bayesian and frequentist point estimators. Their findings indicate that Bayesian point estimators work well in more situations than were previously suspected. In particular, their comparison reveals how a Bayesian point estimator can improve upon a frequentist point estimator even in situations where sharp prior knowledge is not necessarily available. In the current paper, we show that similar results hold when comparing Bayesian and frequentist interval estimators. Furthermore, the development of an appropriate interval esti

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Gao, Xiaoguang, Yu Yang, and Zhigao Gao. "Learning Bayesian networks by constrained Bayesian estimation." Journal of Systems Engineering and Electronics 30, no. 03 (2019): 511–24. http://dx.doi.org/10.21629/jsee.2019.03.09.

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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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Shim, Heejung, and Bret Larget. "BayesCAT: Bayesian co-estimation of alignment and tree." Biometrics 74, no. 1 (2017): 270–79. http://dx.doi.org/10.1111/biom.12640.

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