Modelling road accident blackspots data with the discrete generalized Pareto distribution

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003–2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ + 1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov–Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed.     

Bayesian approach to parameter estimation of the generalized pareto distribution

Several methods have been used for estimating the parameters of the generalized Pareto distribution (GPD), namely maximum likelihood (ML), the method of moments (MOM) and the probability-weighted moments (PWM). It is known that for these estimators to exist, certain constraints have to be imposed on the range of the shape parameter,k, of the GPD. For instance, PWM and ML estimators only exist fork>−0.5 andk≤1, respectively. Moreover, and particularly for small sample sizes, the most efficient method to apply in any practical situation highly depends on a previous knowledge of the most likely values ofk. This clearly suggests the use of Bayesian techniques as a way of using prior information onk. In the present work, we address the issue of estimating the parameters of the GPD from a Bayesian point of view. The proposed approach is compared via a simulation study with ML, PWM and also with the elemental percentile method (EPM) which was developed by Castillo and Hadi (1997). The estimation procedure is then applied to two real data sets.     

On the moments of the modified Weibull distribution

In a recent paper, Xie et al. [Reliab Eng Syst Saf 2002;76:279–85] proposed a modification of the Weibull distribution to allow it to exhibit bathtub-shaped failure rate functions. In this note, we derive explicit algebraic formulas for the kth moment of the distribution. The formulas allow one to calculate the moments for a wide range of values of k and the shape parameter of the modified Weibull. They also allow one to calculate the moments more easily with less computational time. Besides being of interest in their own, we believe that these formulas will be useful for any follow-up mathematical study of the proposed distribution.     

A Bayesian generalized Eyring-Weibull accelerated life testing model

In this paper, a novel approach to a Bayesian accelerated life testing model is presented. The Weibull distribution is used as the life distribution and the generalized Eyring model as the time transformation function. This is a model that allows for the use of more than one stressor, whereas other commonly used acceleration models, such as the Arrhenius and power law models, incorporate one stressor. The use of the generalized Eyring-Weibull model developed in this paper is demonstrated in a case study, where Markov chain Monte Carlo methods are utilized to generate samples for posterior inference.     

Two-dimensional problem for a half-space with axi-symmetric distribution in the theory of generalized thermoelastic diffusion

In this work, we study a two-dimensional problem of axi-symmetric distribution of temperatures in a half-space with a permeating substance in contact with the bounding plane in the context of the theory of generalized thermoelastic diffusion with one relaxation time. The surface of the half-space is taken as traction free and subjected to axi-symmetric time-dependent thermal shock. The chemical potential is also assumed to be a known function of time on the bounding plane. The Laplace and Hankel transform techniques are used. The analytical solution in the transform domain is obtained by using a direct approach. The inverse of the double transform is obtained by using a numerical method based on Fourier expansion techniques. Numerical results for the temperature, displacement, stress, concentration, and chemical potential are carried out and represented graphically.     

Attribute control charts using generalized zero‐inflated Poisson distribution

This paper presents a control charting technique to monitor attribute data based on a generalized zero‐inflated Poisson (GZIP) distribution, which is an extension of ZIP distribution. GZIP distribution is very flexible in modeling complicated behaviors of the data. Both the technique of fitting the GZIP model and the technique of designing control charts to monitor the attribute data based on the estimated GZIP model are developed. Simulation studies and real industrial applications illustrate that the proposed GZIP control chart is very flexible and advantageous over many existing attribute control charts. Copyright © 2008 John Wiley & Sons, Ltd.     

基于广义解调时频分析的多分量信号分解方法

广义解调时频分析方法是一种新的信号处理方法，该方法将广义解凋和最大重叠离散小波包变换相结合对复杂信号进行分解，得到若干个瞬时频率和瞬时幅值都具有物理意义的单分量信号，从而获得原始信号完整的时频分布。本文在介绍广义解调时频分析方法的基础上，将该方法用于多分量信号的分析，对该方法进行了改进，给出了由改进的广义解调时频分析方法分解多分量信号的具体步骤，从而由改进后的广义解调时频分析方法不仅可以得到原始信号中各个分量的时域波形，而且还可以得到相同的时频分布。采用改进后的广义解调时频分析方法对仿真信号进行了分析，同时和其它时频分析方法进行了比较，结果表明了该方法的有效性。最后，对广义解调时频分析方法中的相位函数选择问题进行了讨论。     

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

Competing risks model is considered with dependence causes of failure in this paper. When the latent failure times are distributed by a bivariate Gompertz model, statistical inference for the unknown model parameters is studied from classical and Bayesian approaches, respectively. Under a generalized progressive hybrid censoring, maximum likelihood estimators of the unknown parameters together with the associated existence and uniqueness are established, and the approximate confidence intervals are also obtained based on asymptotic likelihood theory via the observed Fisher information matrix. Moreover, Bayes estimates and the highest posterior density credible intervals of the unknown parameters are also provided based on a flexible Gamma–Dirichlet prior, and Monte Carlo sampling method is also derived to compute associated estimates. Finally, simulation studies and a real-life example are given for illustration purposes.     

On the quasi-stationary grain size distribution from two Gamma size distributions in three-dimensional grain growth

Two different grain structures of which the grain size distribution could be well described by the Gamma distribution were generated by the combination of the Laguerre tessellation and Monte Carlo technique. Monte Carlo simulation shows that the three-dimensional grain growth process can evolve to the quasi-stationary state. The quasi-stationary grain size distribution could be well fit by the Gamma distribution with different parameters. The results in literature from various simulation methods support this point.     

Parameter estimation for bivariate Weibull distribution using generalized moment method for reliability evaluation

Bivariate Weibull distribution can address the life of a system exhibiting 2‐dimensional characteristics in risk and reliability engineering. The applicability of bivariate Weibull distribution has been hindered by its difficulty with parameter estimation, as the number of parameters in bivariate Weibull distribution is more than those in univariate Weibull distribution. Considering a particular structure of a bivariate Weibull distribution model, this paper proposes a generalized moment method (GMM) for parameter estimation. This GMM method is simple, and it has proved to be efficient. The GMM can guarantee the existence and the uniqueness of the solution. A confidence interval for each estimator is derived from the moments of the bivariate distribution. The paper presents a simulation case and 2 real cases to demonstrate the proposed methods.     

基于广义高斯噪声模型的低频线谱检测

研究浅海环境噪声中低频线谱的检测问题.首先对实测海洋环境噪声的统计特性进行分析,提出了应用广义高斯分布对海洋环境噪声建模方法.在此基础上,针对低频线谱检测问题,推导了广义高斯噪声场中的最大似然检测器和渐近检测器,给出了弱线谱条件下线谱频率的最大似然估计.最后,利用实测数据对三种检测器的线谱检测性能进行了验证,结果表明:...     

Interval estimation for inverse Gaussian distribution

In this paper, we consider interval estimation for the inverse Gaussian (IG) distribution. Using generalized pivotal quantity method, we derive the generalized confidence intervals (GCIs) for the model parameters and some quantities such as the quantile, the reliability function of the lifetime, the failure rate function, and the mean residual lifetime. We verify that the GCI of the scale parameter is the same as its commonly used exact CI. We also obtain the generalized prediction intervals (GPIs) for future failure times based on the observed failure data set. In addition, we get the GCI for the reliability of the stress–strength model when the stress and strength variables follow the IG distributions with different parameters. We compare the proposed GCIs and GPIs with the Wald CIs and bootstrap-$p$ CIs by simulation. The simulation results show that the proposed GCIs and GPIs are superior to the Wald CIs and the bootstrap-p CIs in terms of the coverage probability. Finally, two examples are used to illustrate the proposed procedures.     

The generalized EPC method for the non-stationary probabilistic response of nonlinear dynamical system

It is well known that the non-stationary response probability density function (PDF) plays an important role in the reliability and failure analysis. With current approximate or numerical methods, the non-stationary approximate PDF is generally obtained in terms of the ones at the discrete time instants. Repeated computation must be conducted for the ones at other time instants since the response PDF at only one time instant can be obtained after performing the solution procedure. Thus, the computational efficiency suffers a major setback. In this paper, the exponential polynomial closure (EPC) method is further improved and enhanced to obtain the completely non-stationary PDF solution, which is distributed continuously in the time domain. It takes the temporal base function into the PDF approximation. The unknown coefficients in the EPC solution are generalized to be explicit functions of the time parameter. With the least squares method, the explicit time functions can be determined based on the few simulated response PDF values. This implementation makes the continues PDF distribution in the time domain available, which greatly improves the computational efficiency without the repeating computation. Two typical nonlinear systems under stationary and non-stationary random excitations are taken as examples to illustrate the efficiency of the proposed method. Numerical results show that the results obtained by the proposed method agree well with the simulated results. In addition, the relationship between the explicit time function and the modulate function is discussed.     

A variant of the subspace iteration algorithm for generalized eigenproblems

A variant of the subspace iteration algorithm for the generalized eigenproblem, Kp = λ Mp , is proposed. The algorithm does not require the computation of the projected stiffness matrix, and hence, the projected eigenproblem turns out to be a standard eigenproblem as against the generalized eigenproblem of the classical algorithm. This results in appreciable saving in the number of operations of the Rayleigh–Ritz step and hence the solution time, particularly when a large number of eigenvalues are to be computed. The numerical experiments suggest that the proposed algorithm can yield a saving in solution time of up to 28% for certain eigenproblems. Copyright © 2003 John Wiley & Sons, Ltd.     

A nonintrusive proper generalized decomposition scheme with application in biomechanics

Proper generalized decomposition (PGD) is often used for multiquery and fast‐response simulations. It is a powerful tool alleviating the curse of dimensionality affecting multiparametric partial differential equations. Most implementations of PGD are intrusive extensions based on in‐house developed FE solvers. In this work, we propose a nonintrusive PGD scheme using off‐the‐shelf FE codes (such as certified commercial software) as an external solver. The scheme is implemented and monitored by in‐house flow‐control codes. A typical implementation is provided with downloadable codes. Moreover, a novel parametric separation strategy for the PGD resolution is presented. The parametric space is split into two‐ or three‐dimensional subspaces, to allow PGD technique solving problems with constrained parametric spaces, achieving higher convergence ratio. Numerical examples are provided. In particular, a practical example in biomechanics is included, with potential application to patient‐specific simulation.     

Discrete generalized Liouville-Type distribution and related multivariate distributions

Summary Discrete analogue of the Liouville distribution is defined and is termed as Discrete generalized Liouville-Type distribution (DGL-TD) Firstly, properties in its factorial and ordinary momen’s are given. Then by finding the covariance matrix, partial and multiple correlations for DGL-TD are evaluated. Multinomial, multivariate negative binomal and multivariate log series distributions are shown as particular cases of this general distribution. The asymptotic distribution of the estimates of the parameters is also attempted.     

A generalized phase field multiscale finite element method for brittle fracture

A generalized multiscale finite element method is introduced to address the computationally taxing problem of elastic fracture across scales. Crack propagation is accounted for at the microscale utilizing phase field theory. Both the displacement-based equilibrium equations and phase field state equations at the microscale are mapped on a coarser scale. The latter is defined by a set of multinode coarse elements, where solution of the governing equations is performed. Mapping is achieved by employing a set of numerically derived multiscale shape functions. A set of representative benchmark tests is used to verify the proposed procedure and assess its performance in terms of accuracy and efficiency compared with the standard phase field finite element implementation.     

Performance study of terrestrial multi-hop OFDM FSO communication systems with pointing errors over turbulence channels

Abstract

The free-space optical communication systems attract significant research and commercial interest the last few years, due to their high performance and reliability characteristics along with their, relatively, low installation and operational cost. Moreover, due to the fact that these systems are using the atmosphere as propagation path, their performance is varying according to its characteristics. Here, we present the performance analysis of a serially relayed radio-on-free-space-optical (RoFSO) communication system which employs the orthogonal frequency division multiplexing technique, with a quadrature amplitude modulation scheme, over atmospheric turbulence channels modelled by either the Gamma–Gamma or the Gamma distribution model. For this RoFSO communication link, we derive closed-form mathematical expressions for the estimation of its average bit error rate and outage probability, taking into account the relays’ number, the atmospheric turbulence and the pointing errors effect. Furthermore, for realistic parameter values, numerical results are presented using the derived mathematical expressions, which are verified through the corresponding numerical simulations.     

A generalized boundary integral equation for isotropic heat conduction with spatially varying thermal conductivity

In this paper we derive a generalized fundamental solution for the BEM solution of problems of steady state heat conduction with arbitrarily spatially varying thermal conductivity. This is accomplished with the aid of a singular nonsymmetric generalized forcing function, D, with special sampling properties. Generalized fundamental solutions, E, are derived as locally radially symmetric responses to this nonsymmetric singular forcing function, D, at a source point ξ. Both E and D are defined in terms of the thermal conductivity of the medium. Although locally radially symmetric, E varies within the domain as the source point, ξ changes position. A boundary integral equation is formulated. Examples of generalized fundamental solutions are provided for various thermal conductivities along with the corresponding forcing function, D. Here, four numerical examples are provided. Excellent results are obtained with our formulation for variations of thermal conductivity ranging from quadratic and cubic in one dimension to exponential in two dimensions. Problems are solved in regular and irregular regions. Current work is under way investigating extensions of this general approach to further applications where nonhomogeneous property variations are an important consideration.     

Inference for two-parameter Rayleigh competing risks data under generalized progressive hybrid censoring

In this paper, a competing risks model based on the generalized progressive hybrid censored two-parameter Rayleigh distributions is studied under the assumption that the lifetime distributions of failure causes are identically distributed with same location and different scale parameters. We obtain maximum likelihood estimates of unknown parameters with associated existence uniqueness. The approximate confidence intervals are constructed using the asymptotic distribution of maximum likelihood estimates via the observed information matrix. Further, Bayes point estimates and the highest probability density credible intervals of unknown parameters are presented, and the Gibbs sampling technique is used to approximate corresponding estimates. A Monte Carlo simulation study is conducted to compare the accuracy of proposed estimates. Finally, a real-life example is presented for illustration purpose.