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Control Theory and Technology 2024年第1期22卷 69-80页

作者： Junqi Yang Jianhao Xie Feiyang Liu School of Electrical Engineering and Automation Henan Key Laboratory of Intelligent Detection and Control of Coal Mine EquipmentHenan Polytechnic UniversityJiaozuo454003HenanChina

For a class of time-delay discrete-time linear systems with external disturbance and measurement noise, the interval estimation problems of state and measurement noise are investigated in this paper. First, the system state together with the time-delay term and measurement noise is augmented as a new state, and a singular system is then constructed. Subsequently, a kind of decoupling technique is employed to eliminate the effect of external disturbance, and an observer is designed to simultaneously estimate the system state and measurement noise. Based on the estimated state and measurement noise, the interval estimations of system state and measurement noise are obtained by reachability analysis technique. Finally, the effectiveness of the proposed method is verified by a four-tank liquid level system.

关键词： interval estimation Time-delay system Observer design Reachability analysis

Reliability of a Multicomponent Stress-strength Model Based on a Bivariate Kumaraswamy Distribution with Censored Data

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Acta Mathematicae Applicatae Sinica 2024年第2期40卷 478-507页

作者： Cong-hua CHENG School of Mathematics and Statistics Zhaoqing UniversityZhaoqing 526061China

In this paper,we consider a system which has k statistically independent and identically distributed strength components and each component is constructed by a pair of statistically dependent elements with doubly type-II censored scheme.These elements(X1,Y1),(X2,Y2),…,(Xk,Yk)follow a bivariate Kumaraswamy distribution and each element is exposed to a common random stress T which follows a Kumaraswamy distribution.The system is regarded as operating only if at least s out of k(1≤s≤k)strength variables exceed the random stress.The multicomponent reliability of the system is given by Rs,k=P(at least s of the(Z1,…,Zk)exceed T)where Zi=min(Xi,Yi),i=1,…,k.The Bayes estimates of Rs,k have been developed by using the Markov Chain Monte Carlo methods due to the lack of explicit forms.The uniformly minimum variance unbiased and exact Bayes estimates of Rs,k are obtained analytically when the common second shape parameter is known.The asymptotic confidence interval and the highest probability density credible interval are constructed for Rs,k.The reliability estimators are compared by using the estimated risks through Monte Carlo simulations.

关键词： stress-strength model bivariate Kumaraswamy distribution multicomponent reliability doubly Type-II censored scheme interval estimation

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interval estimation in a Two Parameter Weibull Distribution Based on Type-2 Censored Data

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Open Journal of Statistics 2020年第6期10卷 1039-1056页

作者： Raphael Masila Mweleli Luke Akong’o Orawo Cox Lwaka Tamba Justin Obwoge Okenye Department of Mathematics Egerton University Egerton Kenya

In this paper, we consider the construction of the approximate profile- likelihood confidence intervals for parameters of the 2-parameter Weibull distribution based on small type-2 censored samples. In previous research works, the traditional Wald method has been used to construct approximate confidence intervals for the 2-parameter Weibull distribution under type-2 censoring scheme. However, the Wald technique is based on normality assumption and thus may not produce accurate interval estimates for small samples. The profile-likelihood and Wald confidence intervals are constructed for the shape and scale parameters of the 2-parameter Weibull distribution based on simulated and real type-2 censored data, and are hence compared using confidence length and coverage probability.

关键词： Two-Parameter Weibull Distribution interval estimation Relative Likelihood Function Maximum Relative Likelihood Function Profile-Likelihood interval Coverage Probability

Research on the interval estimation of Aircraft Fuel Consumption based on the Data Distribution Characteristics

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Research on the Interval Estimation of Aircraft Fuel Consump...

Bayesian Set estimation with Alternative Loss Functions: Optimality and Regret Analysis

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作者： Xi Wang Jingjie Chen College of Computer Science and Technology Civil Aviation University of China Research Centre for Environment and Sustainable Development of Civil Aviation Administration of China Civil Aviation University of China College of Electronic Information & Automation Civil Aviation University of China National Engineering Laboratory for Integrated Traffic Data Application Technology Civil Aviation University of China

The interval estimation of aircraft fuel consumption is an important basis for airline system planning and operation decision.In order to solve the problem that the dispersion characteristics of actual payloads,itinerary difference in traditional fuel consumption estimation,and the uncertainty of operating environment and pilot accustomization and other factors are not fully considered,which affects the overall interval estimation results,this paper proposed an aircraft fuel consumption interval estimation method based on data deviation and density distribution under-sampling(US-D-DD).For all itineraries of the same type of aircraft,considering the deviation and density distribution of the data,the relevance vector machine(RVM) is used to establish the aircraft fuel consumption interval estimation model,and the aircraft fuel consumption interval estimation results with a certain level of confidence are obtained.Finally,the comprehensive evaluation index is given,and the results before and after considering the data distribution characteristics are compared.The experimental results show that the method achieves better interval estimation effect and verifies its effectiveness.

关键词： Aircraft Fuel Consumption interval estimation Deviation Density Distribution

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Open Journal of Statistics 2023年第2期13卷 195-211页

作者： Fulvio De Santis Stefania Gubbiotti Department of Statistical Sciences Sapienza University of Rome Rome Italy

Decision-theoretic interval estimation requires the use of loss functions that, typically, take into account the size and the coverage of the sets. We here consider the class of monotone loss functions that, under quite general conditions, guarantee Bayesian optimality of highest posterior probability sets. We focus on three specific families of monotone losses, namely the linear, the exponential and the rational losses whose difference consists in the way the sizes of the sets are penalized. Within the standard yet important set-up of a normal model we propose: 1) an optimality analysis, to compare the solutions yielded by the alternative classes of losses;2) a regret analysis, to evaluate the additional loss of standard non-optimal intervals of fixed credibility. The article uses an application to a clinical trial as an illustrative example.

关键词： Bayesian Inference Decision-Theoretic Approach Highest Posterior Density Sets interval estimation Regret

Immune Clone Maximum Likelihood estimation of Improved Non-homogeneous Poisson Process Model Parameters

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Journal of Donghua University(English Edition) 2014年第6期31卷 801-804页

作者：任丽娜芮执元雷春丽 School of Mechanical and Electronical Engineering Lanzhou University of Technology

Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. The minimum negative log-likelihood function was used as the objective function to optimize instead of using iterative method to solve complex system of equations,and the problem of parameter estimation of improved NHPP model was solved by immune clone algorithm. And the interval estimation of reliability indices was given by using fisher information matrix method and delta method. An example of failure truncated data from multiple numerical control( NC) machine tools was taken to prove the method. and the results show that the algorithm has a higher convergence rate and computational accuracy, which demonstrates the feasibility of the method.

关键词： improved non-homogeneous Poisson process immune clone algorithm maximum likelihood estimation(MLE) interval estimation multiple NC machine tools

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Statistical Inference of Stress-Strength Model for Exponentiated Pareto Model with Censored Data

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Journal of Donghua University(English Edition) 2020年第4期37卷 349-356页

作者：程从华 School of Mathematics and Statistics Zhaoqing UniversityZhaoqing 526061China

The reliability of a system is discussed when the strength of the system and the stress imposed on it are independent and non-identical exponentiated Pareto distributed random variables with progressively censored scheme.Different interval estimations are proposed.The interval estimations obtained are exact,approximate and bootstrap confidence intervals.Different methods and the corresponding confidence intervals are compared using Monte-Carlo simulations.Simulation results show that the confidence intervals(CIs)of exact and approximate methods are really better than those of the bootstrap method.

关键词： stress-strength model exponentiated Pareto distribution interval estimation progressively censored scheme

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Reliability of Parallel Stress-Strength Model

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Journal of Mathematical Research with Applications 2018年第4期38卷 427-440页

作者： Conghua CHENG School of Mathematics and Statistics Zhaoqing University

In this paper,the reliability of a parallel stress-strength model of exponentiated Pareto distribution is discussed.Different point estimations and interval estimations are proposed.The point estimators obtained are maximum likelihood and Bayesian estimators.The interval estimations obtained are approximate,exact,bootstrap-p and bootstrap-t confidence intervals and Bayesian credible interval.Different methods and the corresponding confidence intervals are demonstrated using some simulation studies.

关键词： parallel stress-strength model exponentiated Pareto distribution maximum likelihood estimation Bayesian estimation interval estimation

认知诊断属性分类一致性信度区间估计三种方法

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心理学

心理学 2019年第3期 1492-1499页

作者：汪文义方小婷叶宝娟

引入了三种估计认知诊断属性分类一致性信度置信区间的方法：Bootstrap法、平行测验法和平行测验配对法。用模拟研究验证和比较了这三种方法的表现，结果发现，平行测验法和Bootstrap法在被试量较少、题目数量较少的情况下，估计的标准... 详细信息

引入了三种估计认知诊断属性分类一致性信度置信区间的方法：Bootstrap法、平行测验法和平行测验配对法。用模拟研究验证和比较了这三种方法的表现，结果发现，平行测验法和Bootstrap法在被试量较少、题目数量较少的情况下，估计的标准误和置信区间较接近，但是随着被试量的增加，Bootstrap法的估计精度提高较快，在被试量大和题目数量较多时基本接近平行测验配对法的结果。Bootstrap法的所需时间最少，计算过程简单，平行测验配对法计算过程复杂且用时较长，推荐用Bootstrap法估计认知诊断属性分类一致性信度的置信区间。

关键词：属性分类一致性信度区间估计Bootstrap法平行测验法平行测验配对法　　attribute-level classification consistency interval estimation Bootstrap method parallel test method parallel test pairing method

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中国基尼系数是否真地下降了？