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Reducing variance of measurement in optical sensing based on self-Bayesian estimation 认领 引用
1
作者 Xuezhi Zhang Shengliang Zhang +5 位作者 Junfeng Jiang Kun Liu Jiahang Jin Wenxin Bo Ruofan Wang Tiegen Liu 《Advanced Photonics Nexus》 CSCD 2025年第2期64-74,共11页
In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,wh... In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,which is expected to help further enhance sensing precision.We propose a self-Bayesian estimate of the method,aiming at reducing measurement variance.This method utilizes the intensity and phase of the parameter to be measured,achieving statistical optimization of the estimated value through Bayesian inference,effectively reducing the measurement variance.To demonstrate the effectiveness of this method,we adopted an optical fiber heterodyne interference sensing vibration measurement system.The experimental results show that the signal-to-noise ratio is effectively improved within the frequency range of 200 to 500 kHz.Moreover,it is believed that the self-Bayesian estimation method holds broad application prospects in various types of optical sensing. 展开更多
关键词 optical sensing Bayesian estimation signal processing
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SAR Images Despeckling Based on Bayesian Estimation and Fuzzy Shrinkage in Wavelet Domains 认领 引用 被引量：3
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作者 吴艳 王霞 廖桂生 《Chinese Journal of Aeronautics》 EI CAS 2006年第4期326-333,共8页
An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance i... An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance is modeled with a mixture density of two zero-mean Gaussian distributions. A fuzzy shrinkage factor is derived based on the minimum mean square error （MMSE） criteria with Bayesian estimation. In the case above, the ideas of region division and fuzzy shrinkage arc adopted according to the interscale dependencies among wavelet coefficients. The noise-free wavelet coefficients are estimated accurately. Experimental results show that the algorithm proposed is superior to the refined Lee filter, wavelet soft thresbolding shrinkage and SWT shrinkage algorithms in terms of smoothing effects and edges preservation. 展开更多
关键词 SAR image despeclding fuzzy shrinkage factor MMSE region division. Bayesian estimation SWT
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Single channel signal component separation using Bayesian estimation 认领 引用 被引量：4
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作者 Cai Quanwei Wei Ping Xiao Xianci 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第1期33-39,共7页
A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper.By using the basis function projection,the component separation becomes a problem of limited p... A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper.By using the basis function projection,the component separation becomes a problem of limited parameter estimation.Then,a Bayesian model for estimating parameters is set up.The reversible jump MCMC(Monte Carlo Markov Chain)algorithmis adopted to perform the Bayesian computation.The method can jointly estimate the parameters of each component and the component number.Simulation results demonstrate that the method has low SNR threshold and better performance. 展开更多
关键词 Signal component separation Single channel Bayesian estimation Reversible jump MCMC
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Bayesian Estimation and Hierarchical Bayesian Estimation of Zero-failure Data 认领 引用 被引量：11
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作者 韩明 《Chinese Quarterly Journal of Mathematics》 2001年第1期65-70,共6页
The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at... The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at moment t i , if the prior distribution of the failure probability p i=p{T【t i} is quasi_exponential distribution, the author gives the p i Bayesian estimation and hierarchical Bayesian estimation and the reliability under zero_failure date condition is also obtained. 展开更多
关键词 reliability zero_failure data failure probability Bayesian estimation hierarchical Bayesian estimaiton
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E-Bayesian Estimation of the Products Reliability when Testing Reveals no Failure 认领 引用 被引量：2
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作者 韩明 《Chinese Quarterly Journal of Mathematics》 2009年第3期407-414,共8页
This paper develops a new method, named E-Bayesian estimation method, to estimate the reliability parameters. The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product ... This paper develops a new method, named E-Bayesian estimation method, to estimate the reliability parameters. The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product with Binomial distribution. Firstly, for the product reliability, the definitions of E-Bayesian estimation were given, and on the base, expressions of the E-Bayesian estimation and hierarchical Bayesian estimation of the products reliability was given. Secondly, discuss properties of the E-Bayesian estimation. Finally, the new method is applied to a real zero-failure data set, and as can be seen, it is both efficient and easy to operate. 展开更多
关键词 reliability zero-failure data E-Bayesian estimation hierarchical Bayesian estimation
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Dynamic Bayesian estimation of displacement parameters of continuous curve box based on Novozhilov theory 认领 引用
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作者 张剑 叶见曙 赵新铭 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2007年第1期87-95,共9页
The finite strip controlling equation of pinned curve box was deduced on basis of Novozhilov theory and with flexibility method, and the problem of continuous curve box was resolved. Dynamic Bayesian error function of... The finite strip controlling equation of pinned curve box was deduced on basis of Novozhilov theory and with flexibility method, and the problem of continuous curve box was resolved. Dynamic Bayesian error function of displacement parameters of continuous curve box was found. The corresponding formulas of dynamic Bayesian expectation and variance were derived. After the method of solving the automatic search of step length was put forward, the optimization estimation computing formulas were also obtained by adapting conjugate gradient method. Then the steps of dynamic Bayesian estimation were given in detail. Through analysis of a Classic example, the criterion of judging the precision of the known information is gained as well as some other important conclusions about dynamic Bayesian stochastic estimation of displacement parameters of continuous curve box. 展开更多
关键词 displacement parameters Bayesian estimation Novozhilov theory continuous curve box
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Bayesian estimation for nonlinear stochastic hybrid systems with state dependent transitions 认领 引用
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作者 Shunyi Zhao Fei Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第2期242-249,共8页
The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov ... The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system, few liter- ature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions. According to this problem, a new methodology which relaxes quite a restrictive as- sumption that the mode transition process must satisfy Markov properties is proposed. In this method, a general approach is presented to model the state dependent transitions, the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline. Then maximum a posterior estimation is obtained by using the Bayesian theory. The efficacy of the estimator is illustrated by a simulated example . 展开更多
关键词 Bayesian estimation nonlinear stochastic hybrid sys- tem state dependent transition cell space.
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E-Bayesian estimation for competing risk model under progressively hybrid censoring 认领 引用 被引量：3
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作者 Min Wu Yimin Shi Yan Wang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2016年第4期936-944,共9页
This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censori... This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censoring scheme（PHCS）. The estimations are obtained based on Gamma conjugate prior for the parameter under squared error（SE） and Linex loss functions. The simulation results are provided for the comparison purpose and one data set is analyzed. 展开更多
关键词 Bayesian estimation expected Bayesian（E-Bayesian） estimation Gompertz distribution Type-I progressively hybrid censoring
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Bayesian Estimation for the Order of INAR(q)Model 认领 引用 被引量：1
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作者 MIAO GUAN-HONG WANG DE-HUI 《Communications in Mathematical Research》 CSCD 2016年第4期325-331,共7页
In this paper, we consider the problem of determining the order ofINAR（Q） model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss fun... In this paper, we consider the problem of determining the order ofINAR（Q） model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss function. The consistency of the estimator is discussed. The results of a simulation study for the estimation method are presented. 展开更多
关键词 INAR（Q） model Bayesian estimation squared-error loss function con-sistency
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Speech Signal Detection Based on Bayesian Estimation by Observing Air-Conducted Speech under Existence of Surrounding Noise with the Aid of Bone-Conducted Speech 认领 引用 被引量：1
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作者 Hisako Orimoto Akira Ikuta Kouji Hasegawa 《Intelligent Information Management》 2021年第4期199-213,共15页
In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-wri... In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult, some countermeasure methods for surrounding noise are indispensable. In this study, a signal detection method to remove the noise for actual speech signals is proposed by using Bayesian estimation with the aid of bone-conducted speech. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal is theoretically derived. In the proposed speech detection method, bone-conducted speech is utilized in order to obtain precise estimation for speech signals. The effectiveness of the proposed method is experimentally confirmed by applying it to air- and bone-conducted speeches measured in real environment under the existence of surrounding background noise. 展开更多
关键词 Speech Signal Detection Bayesian Estimation Air- and Bone-Conducted Speeches Surrounding Noise
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Weibull-Bayesian Estimation Based on Maximum Ranked Set Sampling with Unequal Samples 认领 引用
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作者 B. S. Biradar B. K. Shivanna 《Open Journal of Statistics》 2016年第6期1028-1036,共9页
A modification of ranked set sampling (RSS) called maximum ranked set sampling with unequal sample (MRSSU) is considered for the Bayesian estimation of scale parameter α of the Weibull distribution. Under this method... A modification of ranked set sampling (RSS) called maximum ranked set sampling with unequal sample (MRSSU) is considered for the Bayesian estimation of scale parameter α of the Weibull distribution. Under this method, we use Linex loss function, conjugate and Jeffreys prior distributions to derive the Bayesian estimate of α. In order to measure the efficiency of the obtained Bayesian estimates with respect to the Bayesian estimates of simple random sampling (SRS), we compute the bias, mean squared error (MSE) and asymptotic relative efficiency of the obtained Bayesian estimates using simulation. It is shown that the proposed estimates are found to be more efficient than the corresponding one based on SRS. 展开更多
关键词 Bayesian Estimation Loss Function MRSSU SRS RSS
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A Noise Suppression Method for Speech Signal by Jointly Using Bayesian Estimation and Fuzzy Theory 认领 引用
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作者 Akira Ikuta Hisako Orimoto Kouji Hasegawa 《Journal of Software Engineering and Applications》 2021年第12期631-645,共15页
Speech recognition systems have been applied to inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult. In these a... Speech recognition systems have been applied to inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult. In these actual circumstances, some countermeasure methods for surrounding noise are indispensable. In this study, a new method to remove the noise for actual speech signal was proposed by using Bayesian estimation with the aid of bone-conducted speech and fuzzy theory. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal was theoretically derived. In the proposed noise suppression method, bone-conducted speech signal with the reduced high-frequency components was regarded as fuzzy observation data, and a stochastic model for the bone-conducted speech was derived by applying the probability measure of fuzzy events. The proposed method was applied to speech signals measured in real environment with low SNR, and better results were obtained than an algorithm based on observation of only air-conducted speech. 展开更多
关键词 Air- and Bone-Conducted Speeches Noise Suppression Bayesian Estimation Fuzzy Data
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Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness 认领 引用
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作者 Atsuo Murata Yohei Uragami 《Journal of Traffic and Transportation Engineering》 2018年第1期1-15,共15页
The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants... The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash. 展开更多
关键词 Bayesian estimation drowsy driving simulated driving task tracking error physiological measure crash risk.
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TopoEKF:From State-Space Estimation to Topological Signatures for Enhanced Multi-Object Tracking and Anomaly Detection in UAVs 认领 引用
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作者 Rabia Kıratlı Hatice Unlu Eroğlu Alperen Eroğlu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期860-908,共49页
Reliable multi-object detection and tracking play a critical role in Unmanned Aerial Vehicles-based aerial surveillance applications operating under challenging real-world conditions.This study presents a mathematical... Reliable multi-object detection and tracking play a critical role in Unmanned Aerial Vehicles-based aerial surveillance applications operating under challenging real-world conditions.This study presents a mathematically grounded,model-driven tracking framework named TopoEKF,which integrates an enhanced Adaptive Extended Kalman Filter with Topological Data Analysis to improve both tracking robustness and anomaly detection performance.Unlike prior approaches that primarily focus on refining object detection architectures,this work emphasizes the predictive power of iterative Bayesian filtering,optimal state estimation,and adaptive error minimization within a unified mathematical framework.The proposed system employs a carefully optimized YOLOvl2 detector to provide accurate object location priors,followed by a formally defined discrete-time linear Gaussian tracking model.The Adaptive EKF is leveraged to handle nonlinearities arising from the projection of three-dimensional object motion onto the two-dimensional image plane through local linearization.To further enhance robustness under low resolution,large object-to-image distances,frequent occlusions,and environmental noise,TopoEKF introduces adaptive noise covariance modeling driven by measurement confidence,occlusion status,and topological feedback.Persistent homology is applied to EKF-filtered trajectories to extract topological signatures that characterize the global structure of object motion.These features are transformed into fixed-dimensional representations and processed by an unsupervised Isolation Forest classifier for trajectory-level anomaly detection.Experimental evaluations are conducted on a challenging hybrid dataset combining scenarios from COCO,VisDrone,UAVDT,Road_Anomaly_Dataset,and DoTA benchmarks.Quantitative results demonstrate that TopoEKF improves Multi-Object Tracking Accuracy from 72.8%to 76.3%and reduces identity switches by approximately 34%compared to a standard EKF baseline.The enhanced EKF achieves up to 20%higher robustness in highly noisy and indoor environments while maintaining realtime performance at 28.5 frames per second on resource-constrained embedded platforms.In the anomaly detection stage,the integration of persistent homology-based features improves the F1-score from 66%to 84%,with substantial gains in both precision and recall.Overall,the proposed approach highlights the effectiveness of interpretable,mathematically founded state estimation models as a reliable and efficient alternative to black-box deep learning systems in safety-critical UAV applications. 展开更多
关键词 Extended Kalman filter UAV tracking multi-object tracking mathematical modeling Bayesian estimation YOLOvl2 topological data analysis persistent homology
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Bayesian phase difference estimation based on single-photon projective measurement 认领 引用
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作者 Xu-Hao Yu Ying Wei +7 位作者 Ran Yang Wen-Hui Song Yingning Miao Wei Zhou Xinhui Li Xiaoqin Gao Yan-Xiao Gong Shi-Ning Zhu 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第7期89-93,共5页
The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale ... The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale quantum(NISQ)devices due to their excessive depth and circuit complexity.We demonstrate a high-precision phase difference estimation protocol based on the Bayesian phase difference estimation algorithm and single-photon projective measurement.The iterative framework of the algorithm,combined with the independence from controlled unitary operations,inherently mitigates circuit depth and complexity limitations.Through an experimental realization on the photonic system,we demonstrate high-precision estimation of diverse phase differences,showing root-mean-square errors(RMSE)below the standard quantum limit(1/√N)and reaching the Heisenberg scaling(1/N)after a certain number of iterations.Our scheme provides a critical advantage in quantum resource-constrained scenarios,and advances practical implementations of quantum information tasks under realistic hardware constraints. 展开更多
关键词 Bayesian phase difference estimation single-photon projection measurement Heisenberg limit quantum information quantum state engineering and measurements
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A fault segment location method for distribution networks based on spiking neural P systems and Bayesian estimation 认领 引用 被引量：12
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作者 Yi Wang Tao Wang Liyuan Liu 《Protection and Control of Modern Power Systems》 SCIE EI 2023年第3期184-195,共12页
With the increasing scale of distribution networks and the mass access of distributed generation,traditional central-ized fault location methods can no longer meet the performance requirements of speed and high accura... With the increasing scale of distribution networks and the mass access of distributed generation,traditional central-ized fault location methods can no longer meet the performance requirements of speed and high accuracy.There-fore,this paper proposes a fault segment location method based on spiking neural P systems and Bayesian estimation for distribution networks with distributed generation.First,the distribution network system topology is decoupled into single-branch networks.A spiking neural P system with excitatory and inhibitory synapses is then proposed to model the suspected faulty segment,and its matrix reasoning algorithm is executed to obtain a preliminary set of location results.Finally,the Bayesian estimation and contradiction principle are applied to verify and correct the ini-tial results to obtain the final location results.Simulation results based on the IEEE 33-node system validate the feasi-bility and effectiveness of the proposed method. 展开更多
关键词 Distribution network Fault location Spiking neural P system Bayesian estimation Contradiction principle
Target threat estimation based on discrete dynamic Bayesian networks with small samples 认领 引用 被引量：7
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作者 YE Fang MAO Ying +1 位作者 LI Yibing LIU Xinrui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第5期1135-1142,共8页
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target thr... The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications. 展开更多
关键词 discrete dynamic Bayesian network(DDBN) parameter learning missing data filling Bayesian estimation
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Information flow among neural networks with Bayesian estimation 认领 引用 被引量：1
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作者 LI Yan LI XiaoLi +1 位作者 OUYANG GaoXiang GUAN XinPing 《Chinese Science Bulletin》 2007年第14期2006-2011,共6页
Estimating the interaction among neural networks is an interesting issue in neuroscience.Some methods have been proposed to estimate the coupling strength among neural networks;however,few estimations of the coupling ... Estimating the interaction among neural networks is an interesting issue in neuroscience.Some methods have been proposed to estimate the coupling strength among neural networks;however,few estimations of the coupling direction(information flow)among neural networks have been attempted.It is known that Bayesian estimator is based on a priori knowledge and a probability of event occurrence.In this paper,a new method is proposed to estimate coupling directions among neural networks with conditional mutual information that is estimated by Bayesian estimation.First,this method is applied to analyze the simulated EEG series generated by a nonlinear lumped-parameter model.In comparison with the conditional mutual information with Shannon entropy,it is found that this method is more successful in estimating the coupling direction,and is insensitive to the length of EEG series.Therefore,this method is suitable to analyze a short time series in practice.Second,we demonstrate how this method can be applied to the analysis of human intracranial epileptic electroencephalogram(EEG)recordings,and to indicate the coupling directions among neural networks.Therefore,this method helps to elucidate the epileptic focus localization. 展开更多
关键词 phase synchronization coupling direction conditional mutual information Bayesian estimation epileptic EEG
Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information 认领 引用
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作者 Myungjin Cho 《Chinese Optics Letters》 SCIE EI CAS CSCD 2015年第7期36-39,共4页
In this Letter, we propose a novel three-dimeusional （3D） color microscopy for microorganisms under photon- starved conditions using photon counting integral imaging and Bayesian estimation with adaptive priori info... In this Letter, we propose a novel three-dimeusional （3D） color microscopy for microorganisms under photon- starved conditions using photon counting integral imaging and Bayesian estimation with adaptive priori infor- mation. In photon counting integral imaging, 3D images can be visualized using maximum likelihood estimation （MLE）. However, since MLE does not consider a priori information of objects, the visual quality of 3D images may not be accurate. In addition, the only grayscale image can be reconstructed. Therefore, to enhance the visual quality of 3D images, we propose photon counting microscopy using maximum a posteriori with adaptive priori information. In addition, we consider a wavelength of each basic color channel to reconstruct 3D color images. To verify our proposed method, we carry out optical experiments. 展开更多
关键词 Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information MLE
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Exponentiated Generalized Inverse Flexible Weibull Distribution:Bayesian and Non-Bayesian Estimation Under Complete and TypeⅡCensored Samples with Applications 认领 引用 被引量：1
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作者 M.El-Morshedy M.S.Eliwa +2 位作者 A.El-Gohary Ehab M.Almetwally R.EL-Desokey 《Communications in Mathematics and Statistics》 SCIE CSCD 2022年第3期413-434,共22页
In this paper,a new 4-parameter exponentiated generalized inverse flexible Weibull distribution is proposed.Some of its statistical properties are studied.The aim of this paper is to estimate the model parameters via ... In this paper,a new 4-parameter exponentiated generalized inverse flexible Weibull distribution is proposed.Some of its statistical properties are studied.The aim of this paper is to estimate the model parameters via several approaches,namely,maximum likelihood,maximum product spacing and Bayesian.According to Bayesian approach,several techniques are used to get the Bayesian estimators,namely,standard error function,Linex loss function and entropy loss function.The estimation herein is based on complete and censored samples.Markov Chain Monte Carlo simulation is used to discuss the behavior of the estimators for each approach.Finally,two real data sets are analyzed to obtain the flexibility of the proposed model. 展开更多
关键词 Weibull distribution Hazard rate function Maximum likelihood estimation Maximum product spacing estimation Bayesian estimation Censored samples Simulation
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