Statistical analysis of generalized exponential distribution under progressive censoring with binomial removals

The estimation of generalized exponential distribution based on progressive censoring with binomial removals is presented, where the number of units removed at each failure time follows a binomial distribution. Maximum likelihood estimators of the parameters and their confidence intervals are derived. The expected time required to complete the life test under this censoring scheme is investigated. Finally, the numerical examples are given to illustrate some theoretical results by means of Monte-Carlo simulation.

Inference for dependence competing risks from bivariate exponential model under generalized progressive hybrid censoring with partially observed failure causes

Inference are considered for the dependence competing risks model by using the Marshal-Olkin bivariate exponential distribution. Under generalized progressively hybrid censoring with partially observed failure causes, the maximum likelihood estimators are established, and the approximate confidence intervals are also constructed via the observed Fisher information matrix.Moreover, Bayes estimates and highest probability density credible intervals are presented and the importance sampling technique is used to compute corresponding results. Finally, the numerical analysis is proposed for illustration.

Exponential Dichotomy and Eberlein-Weak Almost Periodic Solutions

We give sufficient conditions ensuring the existence and uniqueness of an Eberlein-weakly almost periodic solution to the following linear equation dx/dt(t) = A(t)x(t) + f(t) in a Banach space X, where (A(t)) t ∈□ is a family of infinitesimal generators such that for all t ∈□, A(t + T) = A(t) for some T > 0, for which the homogeneuous linear equation dx/dt(t) = A(t)x(t) is well posed, stable and has an exponential dichotomy, and f:□ →X is Eberlein-weakly amost periodic.

Entropy Bayesian Analysis for the Generalized Inverse Exponential Distribution Based on URRSS

This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution.Assuming that the observed samples are taken from the upper record ranked set sampling(URRSS)and upper record values(URV)schemes.Formulas of Bayesian estimators are derived depending on a gamma prior distribution considering the squared error,linear exponential and precautionary loss functions,in addition,we obtain Bayesian credible intervals.The random-walk Metropolis-Hastings algorithm is handled to generate Markov chain Monte Carlo samples from the posterior distribution.Then,the behavior of the estimates is examined at various record values.The output of the study shows that the entropy Bayesian estimates under URRSS are more convenient than the other estimates under URV in the majority of the situations.Also,the entropy Bayesian estimates perform well as the number of records increases.The obtained results validate the usefulness and efficiency of the URV method.Real data is analyzed for more clarifying purposes which validate the theoretical results.

Quadratic Lyapunov Function and Exponential Dichotomy on Time Scales

In this paper, we study the relationship between exponential dichotomy and quadratic Lyapunov function for the linear equation x△=A（t）x on time scales. Moreover, for the nonlinear perturbed equation x△= A（t）x ＋ f（t, x） we give the instability of the zero solution when f is sufficiently small.

Pseudo Almost Automorphic Solutions for Non-autonomous Stochastic Differential Equations with Exponential Dichotomy

In this paper, we consider the existence and uniqueness of the solutions which are pseudo almost automorphic in distribution for a class of non-autonomous stochastic differential equations in a Hilbert space. In conclusion, we use the Banach contraction mapping principle and exponential dichotomy property to obtain our main results.

Unified treatment of one-range addition theorems for integer and non-integer n-STO, -GTO and -generalized exponential type orbitals with hyperbolic cosine in position, momentum and four-dimensional spaces

Simpler formulas are derived for one-range addition theorems for the integer and noninteger n generalized ex- ponential type orbitals, momentum space orbitals, and hyperspherical harmonics with hyperbolic cosine （GETO HC, GMSO HC, and GHSH HC） in position, momentum and four-dimensional spaces, respectively. The final results are expressed in terms of one-range addition theorems of complete orthonormal sets of Ca-exponential type orbitals, Ca- momentum space orbitals and za-hyperspherical harmonics. We notice that the one-range addition theorems for integer and noninteger n-Slater type orbitals and Gaussian type orbitals in position, momentum and four dimensional spaces are special cases of GETO HC, GMSO HC, and GHSH HC. The theorems presented can be useful in the accurate study of the electronic structure of atomic and molecular systems.

On Moment Generating Function of Generalized Order Statistics from Erlang-Truncated Exponential Distribution

In this paper explicit expressions and some recurrence relations are derived for marginal and joint moment generating functions of generalized order statistics from Erlang-truncated exponential distribution. The results for k-th record values and order statistics are deduced from the relations derived. Further, a characterizing result of this distribution on using the conditional expectation of function of generalized order statistics is discussed.

Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations

We consider a time series following a simple linear regression with first-order autoregressive errors belonging to the class of heavy-tailed distributions. The proposed model provides a useful generalization of the symmetrical linear regression models with independent error, since the error distribution covers both correlated innovations following a Generalized Exponential distribution. Furthermore, we derive the modified maximum likelihood (MML) estimators as an efficient alternative for estimating model parameters. Finally, we investigate the asymptotic properties of the proposed estimators. Our findings are also illustrated through a simulation study.

AN EME BLIND SOURCE SEPARATION ALGORITHM BASED ON GENERALIZED EXPONENTIAL FUNCTION

This letter investigates an improved blind source separation algorithm based on Maximum Entropy （ME） criteria. The original ME algorithm chooses the fixed exponential or sigmoid ftmction as the nonlinear mapping function which can not match the original signal very well. A parameter estimation method is employed in this letter to approach the probability of density function of any signal with parameter-steered generalized exponential function. An improved learning rule and a natural gradient update formula of unmixing matrix are also presented. The algorithm of this letter can separate the mixture of super-Gaussian signals and also the mixture of sub-Gaussian signals. The simulation experiment demonstrates the efficiency of the algorithm.

Parameters optimization for exponentially weighted moving average control chart using generalized regression neural network

As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA （MEWMA） control charts, in which the optimal parameter pair （ λ, k） or （ λ, h ） was searched by using the generalized regression neural network （GRNN）. The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length （ARLo） and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.

EXPONENTIAL ATTRACTORS FOR A GENERALIZED GINZBURG-LANDAU EQUATION

Based on the paper [1]. we obtain the existence of exponential attractors for a generalized Ginzburg-Landau equation in one dimension

Generalized Weighted Exponential-Gompertez Distribution

Statistical analysis of lifetime data is a significant topic in social sciences, engineering, reliability, biomedical and others. We use the generalized weighted exponential distribution, as a generator to introduce a new family called generalized weighted exponential-G family, and apply this new generator to provide a new distribution called generalized weighted exponential gombertez distribution. We investigate some of its properties, moment generating function, moments, conditional moments, mean residual lifetime, mean inactivity time, strong mean inactivity time, Rényi entropy, Lorenz curves and Bonferroni. Furthermore, in this model, we estimate the parameters by using maximum likelihood method. We apply this model to a real data-set to show that the new generated distribution can produce a better fit than other classical lifetime models.

指数有界双连续n阶α次积分C半群的扰动

利用经典算子半群理论中的研究方法,在指数有界双连续α次积分C半群的基础上,讨论了指数有界双连续n阶α次积分C半群的扰动定理。设A为次生成元的指数有界双连续n阶α次积分C半群{T(t)}_(t≥0),B为界线性算子,A、B、C可交换,则在一定条件下,C^(-1)(A+B)C_(B)生成双连续n阶α次积分C半群{T_(B)(t)}_(t≥0),并给出T_(B)(t)的表达式,从而推广了n阶α次积分C半群相关的扰动定理。

一个解特定二阶驻定方程的新方法——特征伴随方程法

本文对文献[1]中提出的一类二阶驻定方程G″/G=∑^(m)_(j=0)P_(j)(G′/G)^(j)的求法进行探讨,提出了全新的求解方法——特征伴随方程法,通过该求法得到这类方程当m取不同非负整数(本文仅讨论m=2)时的通解,并相应给出该方程作为求解非线性偏微分方程的辅助方程时所需要的G′/G的解析表达式;同时给出一个作为常微分方程中驻定方程的应用实例,以及该方程作为非线性偏微分方程的辅助方程时利用扩展G′/G展开法的求解例子,通过该实例给出方程的精确行波解。

指数有界n阶α次积分C群的紧性

利用指数有界n阶α次积分C群及其次生成元的定义,并借助经典算子理论的研究方法,给出了指数有界n阶α次积分C群紧的定义,从而推导出指数有界n阶α次积分C群的紧性的一些性质。

广义指数几何分布的扩展及参数估计

在广义指数几何分布的基础上对参数进行扩展,提出了一种新的寿命分布。旨在通过这种扩展,增强模型针对一些数据的拟合能力,从而更准确地进行寿命预测和可靠性分析。采用极大似然估计法对新提出的分布进行参数估计,并通过牛顿迭代法求解复杂的参数估计问题。对选定样本数据进行估计求解,并将模型与实际观测数据进行了对比。模型的拟合优度通过AIC和BIC值与其他模型进行了比较,结果表明新提出的扩展广义指数几何分布在拟合观测数据方面表现出较好的效果。

求解逆混合变分不等式问题的动力系统方法

本文主要研究求解逆混合变分不等式问题的动力系统方法。首先,在强单调和Lipschitz连续性的假设下,通过借助广义f-投影算子的性质,得到逆混合变分不等式问题有唯一解的充分条件。进一步考虑一个动力系统方法来求解逆混合变分不等式问题,而且该动力系统具有唯一均衡点。最后,证明在强单调和Lipschitz连续性的条件下该动力系统的唯一均衡点是全局指数稳定的。

基于“承影”GPGPU的张量处理器设计

针对神经网络对算力和通用性的需求进一步扩大,基于开源项目“承影”GPGPU,设计了张量处理器,可以对卷积、通用矩阵乘进行加速。首先,分析现有张量处理器设计方案及其对应算法,与直接进行卷积计算进行对比,分析性能差异。然后,提出基于三维乘法树结构的张量处理器设计,将其部署在Xilinx VCU128开发板上。在VCU128开发板上,张量处理器的工作频率为222 MHz。同时,开发了指数运算单元,辅助完成神经网络运算。在VCU128开发板上的工作频率为159 MHz。最后,利用编写汇编程序的方法,验证张量处理器的功能正确性。引入张量处理器后,预期运行时间明显减少。

指数有界双参数n阶α次积分C群的紧性

利用指数有界双参数n阶α次积分C群及其次生成元的定义,并借助经典算子理论的研究方法,给出双参数n阶α次积分C群紧的定义,得到指数有界双参数n阶α次积分C群的紧的性质。