The beta exponential distribution

The exponential distribution is perhaps the most widely applied statistical distribution for problems in reliability. In this note, we introduce a generalization—referred to as the beta exponential distribution—generated from the logit of a beta random variable. We provide a comprehensive treatment of the mathematical properties of the beta exponential distribution. We derive expressions for the moment generating function, characteristic function, the first four moments, variance, skewness, kurtosis, mean deviation about the mean, mean deviation about the median, Rényi entropy, Shannon entropy, the distribution of sums and ratios, and the asymptotic distribution of the extreme order statistics. We also discuss simulation issues, estimation by the methods of moments and maximum likelihood and provide an expression for the Fisher information matrix. We hope that this generalization will attract wider applicability in reliability.     

基于分位数函数和Bootstrap分布的不确定度评定研究

最大信息熵方法是基于概率分布评定测量不确度的主要方法之一。其所依赖的高阶矩需要较大样本的测量数据,而校准/检测实验室的测量一般为小样本,故用最大熵方法评定小样本测量不确定度缺乏一定的可靠性。提出了基于分位数函数和概率权重矩作为约束条件的最大信息熵不确定度评定法,把矩的计算从高次降为一次,并结合遗传算法求解概率分布,用Bootstrap分布估计扩展不确定度和包含区间,解决了由分位数区间估计分布不对称所致的复杂计算问题。     

最大熵法可靠度理论在工程中的应用

基于信息论中最大熵的概念，探讨了最大熵法的可靠度理论在工程中的应用。讨论了样本的均值、标准差对收敛性的影响，以及样本数量与最大熵法阶数对概率密度函数、超越概率、可靠指标精度的影响。提出了结构分析中应采用四阶矩法，并给出应用该方法时精度控制的若干建议。     

Tests for exponentiality against theM andLM-Classes of life distributions

This paper studies tests for exponentiality against the nonparametric classesM andLM of life distributions introduced by Klar and Müller (2003). The test statistics are integrals of the difference between the empirical moment generating function of given data and the moment generating function of a fitted exponential distribution. We derive the limit distributions of the test statistics in case of a general underlying distribution and the local approximate Bahadur efficiency of the procedures against several parametric families of alternatives to exponentiality. The finite sample behavior of the tests is examined by means of a simulation study. Finally, the tests under discussion are applied to two data sets, and we discuss the applicability of the tests under random censorship.     

Generalized Marshall Olkin Inverse Lindley Distribution with Applications

In this article, a new generalization of the inverse Lindley distribution is introduced based on Marshall-Olkin family of distributions. We call the new distribution, the generalized Marshall-Olkin inverse Lindley distribution which offers more flexibility for modeling lifetime data. The new distribution includes the inverse Lindley and the Marshall-Olkin inverse Lindley as special distributions. Essential properties of the generalized Marshall-Olkin inverse Lindley distribution are discussed and investigated including, quantile function, ordinary moments, incomplete moments, moments of residual and stochastic ordering. Maximum likelihood method of estimation is considered under complete, Type-I censoring and Type-II censoring. Maximum likelihood estimators as well as approximate confidence intervals of the population parameters are discussed. A comprehensive simulation study is done to assess the performance of estimates based on their biases and mean square errors. The notability of the generalized Marshall-Olkin inverse Lindley model is clarified by means of two real data sets. The results showed the fact that the generalized Marshall-Olkin inverse Lindley model can produce better fits than power Lindley, extended Lindley, alpha power transmuted Lindley, alpha power extended exponential and Lindley distributions.     

Non‐normal Capability Indices for the Weibull and Lognormal Distributions

Because the normal process capability indices (PCIs) C_p, C_pu, C_pl, and C_pk represent the times that the process standard deviation is within the specification limits; then, based on and by using the direct relations among the parameters of the Weibull, Gumbel (minimum extreme value type I) and lognormal distributions, the Weibull and lognormal PCIs are derived in this paper. On the other hand, because the proposed PCIs P_p, P_pu, P_pl, and P_pk were derived as a function of the mean and standard deviation of the analyzed process, they have the same practical meaning with those of the normal distribution. Results show that the proposed PCIs could be used as the standard C_p, C_pu, C_pl, and C_pk if a short‐term variance is analyzed. An application to a set of simulated data is presented. Copyright © 2015 John Wiley & Sons, Ltd.     

Bayesian EWMA control charts based on Exponential and transformed Exponential distributions

In this paper, we proposed the Bayesian exponentially weighted moving average (EWMA) control charts for mean under the nonnormal life time distributions. We used the time between events data which follow the Exponential distribution and proposed the Bayesian EWMA control charts for Exponential distribution and transformed Exponential distributions into Inverse Rayleigh and Weibull distributions. In order to develop the control charts, we used a uniform prior under five different symmetric and asymmetric loss functions (LFs), namely, squared error loss function (SELF), precautionary loss function (PLF), general entropy loss function (GELF), entropy loss function (ELF), and weighted balance loss function (WBLF). The average run length (ARL) and the standard deviation of run length (SDRL) are used to check the performance of the proposed Bayesian EWMA control charts for Exponential and transformed Exponential distributions. An extensive simulation study is conducted to evaluate the proposed Bayesian EWMA control chart for nonnormal distributions. It is observed from the results that the proposed control chart with the Weibull distribution produces the best results among the considered distributions under different LFs. A real data example is presented for implementation purposes.     

About Shewhart control charts to monitor the Weibull mean

In this paper, a new reparametrization expressed in terms of the process mean for Weibull distribution is studied; thus, the monitoring of the process mean can be made directly. Additionally, we call attention that the asymptotic control limits for control chart by central limit theorem (CLT) may lead to a serious erroneous decision. Definitively, they can only be used to signal small/medium shifts in the process mean but with a very very large sample size. We present guidelines for practitioners about the minimum sample size needed to match out‐of‐control average run length (ARL₁) with the exact and asymptotic control limits in function of the shape parameter after an extensive simulation study. The proposed schemes are applied to monitoring the Weibull mean parameter of the strength distribution of a carbon fibber used in composite materials.     

The Principle of Maximum Entropy Applied in the Evaluation of the Measurement Uncertainty

The maximum entropy approach is a flexible and powerful tool for assigning a probability distribution to a measurable quantity treated as a random variable subjected to known moment constraints. The aim of this paper is to describe how the principle of maximum entropy may be used to transform information about the value of a quantity into a probability density function (pdf) that reflects exactly that information and nothing else. This principle will be applied to common cases of metrological interest, where different kinds of information are available. The derivation of the pdf is given in each case, and two practical examples with numerical results are reported to demonstrate the efficiency of the maximum entropy method     

Statistical Analysis for Decision Making

In this article we consider a generalization of the univariate g-and-h distribution to the multivariate situation with the aim of providing a flexible family of multivariate distributions that incorporate skewness and kurtosis. The approach is to modify the underlying random variables and their quantiles, directly giving rise to a family of distributions in which the quantiles rather than the densities are the foci of attention. Using the ideas of multivariate quantiles, we show how to fit multivariate data to our multivariate g-and-h distribution. This provides a more flexible family than the skew-normal and skew-elliptical distributions when quantiles are of principal interest. Unlike those families, the distribution of quadratic forms from the multivariate g-and-h distribution depends on the underlying skewness. We illustrate our methods on Australian athletes data, as well as on some wind speed data from the northwest Pacific.     

Local polynomial estimation of a conditional mean function with dependent truncated data

By applying local polynomial regression, we propose an estimator of a conditional mean function and its derivatives under a left truncation model. The target function includes the regression function, the conditional moment as well as the conditional distribution function as special cases. It is assumed that the observations form a stationary α-mixing sequence. Asymptotic normality of the estimator is established. The finite sample behavior of the estimator is investigated via simulations, too.     

Application of the maximum entropy method in texture analysis

The problem of estimating the crystallite orientation distribution function (codf) based on the leading texture coefficients is considered. Problems of such a type are called moment problems, which are well known in statistical mechanics and other areas of science. It is shown how the maximum entropy method can be applied to estimate the codf. Special emphasis is given to a coordinate-free formulation of the problem. The codf is represented by a tensorial Fourier series. The equations, which have to be solved for the estimate of the distribution function, are derived for all tensor ranks of the Fourier coefficients. As a numerical example, a model codf is estimated based on a set of discrete crystal orientations given by a full-constrained Taylor type texture simulation.     

Identification of Specimen Position and Orientation Using Standard Deviation of Intensity in Phase‐Shifting Digital Holography1

Abstract: Phase‐shifting digital holography is a useful method to measure the displacement distribution and the strain distribution of an object surface. The complex amplitude distribution of an object surface is obtained as the complex amplitude distribution at a reconstruction distance. It is, however, difficult to measure the reconstruction distance by actual measurement. We discovered that the standard deviation of the intensity on the reconstructed image becomes the maximum value when the reconstruction distance is the same as the actual optical path length. The displacement distributions are obtained for the x‐, y‐ and z‐directions. When the normal direction of an object surface inclines from the z‐direction, the displacements defined on the xyz‐coordinate system should be transformed into the object coordinate system. It is, therefore, required to develop a measurement method of the orientation of the object to obtain the parameters for transforming from the xyz‐coordinate system into the object coordinate system. In this paper, the method to identify the position and the orientation of a specimen using the standard deviation of the intensity distribution is proposed.     

Programs in R for computing truncated t distributions

Truncated distributions arise naturally in many industrial settings. Owing to the popularity of the normal distribution, programs have been developed for computing quantities of interest of its truncated version such as the truncated mean, truncated variance and the cumulative probability. In this note, we follow up the work for the Student's t distribution because of its recent popularity as a rival to the normal distribution. We provide programs for computing the mean, variance, cumulative probability and two other quantities of interest for any truncated t distribution: whether it is left truncated, right truncated or doubly truncated. The programs are written in R: a freely downloadable statistical software. Copyright © 2006 John Wiley & Sons, Ltd.     

Impact of main drive system of 5 m wide and heavy plate mill on screw-down load deviation

Universal couplings cause additional moments on the sleeve shaft of work roll. The problem becomes more obvious in the wide and heavy plate mill due to the large tilt angle and the great rolling torque, which may affect the deflection of roll system and the screw-down load deviation. Aiming at the structural characteristics of the drive system of a 5000 mm wide and heavy plate mill, additional moment models acting on the work roll are established, and the influence coefficients of the additional moment of the main drive system are obtained according to Mohr integral method. The screw-down load deviation affected by the additional moment is calculated based on the influence function method. Results show that the additional moment of the work roll drive end has little effect on the screw-down load deviation. And the fluctuation period of the additional moment is 180° with the input shaft rotating. The additional moment enlarges with the tilting angle increasing. If the tilt angle is equal to 4.82°, the vertical additional moment is about 8.5% of the output torque of motor shaft, while the horizontal additional moment is about 4.2% of that.     

Shewhart and EWMA t control charts for short production runs

Short‐run productions are common in manufacturing environments like job shops, which are characterized by a high degree of flexibility and production variety. Owing to the limited number of possible inspections during a short run, often the Phase I control chart cannot be performed and correct estimates for the population mean and standard deviation are not available. Thus, the hypothesis of known in‐control population parameters cannot be assumed and the usual control chart statistics to monitor the sample mean are not applicable. t‐charts have been recently proposed in the literature to protect against errors in population standard deviation estimation due to the limitation of available sampling measures. In this paper the t‐charts are tested for implementation in short production runs to monitor the process mean and their statistical properties are evaluated. Statistical performance measures properly designed to test the chart sensitivity during short runs have been considered to compare the performance of Shewhart and EWMA t‐charts. Two initial setup conditions for the short run fixing the population mean exactly equal to the process target or, alternatively, introducing an initial setup error influencing the statistic distribution have been modelled. The numerical study considers several out‐of‐control process operating conditions including one‐step shifts for the population mean and/or standard deviation. The obtained results show that the t‐charts can be successfully implemented to monitor a short run. Finally, an illustrative example is presented to show the use of the investigated t charts. Copyright © 2010 John Wiley & Sons, Ltd.     

Gamma function tables for the estimation of the mean and standard deviation of the weibull distribution

This paper provides tabulated numerical values for the gamma function necessary for the analytical estimation of the mean and standard deviation of the Weibull distribution. The tables produced also include estimates of the Weibull coefficient of variation, a quantity very important for maintenance optimization calculations when Glasser's charts are employed.     

Random matrix eigenvalue problems in structural dynamics

Natural frequencies and mode shapes play a fundamental role in the dynamic characteristics of linear structural systems. Considering that the system parameters are known only probabilistically, we obtain the moments and the probability density functions of the eigenvalues of discrete linear stochastic dynamic systems. Current methods to deal with such problems are dominated by mean‐centred perturbation‐based methods. Here two new approaches are proposed. The first approach is based on a perturbation expansion of the eigenvalues about an optimal point which is ‘best’ in some sense. The second approach is based on an asymptotic approximation of multidimensional integrals. A closed‐form expression is derived for a general rth‐order moment of the eigenvalues. Two approaches are presented to obtain the probability density functions of the eigenvalues. The first is based on the maximum entropy method and the second is based on a chi‐square distribution. Both approaches result in simple closed‐form expressions which can be easily calculated. The proposed methods are applied to two problems and the analytical results are compared with Monte Carlo simulations. It is expected that the ‘small randomness’ assumption usually employed in mean‐centred‐perturbation‐based methods can be relaxed considerably using these methods. Copyright © 2006 John Wiley & Sons, Ltd.     

Usc/fibred entropy structure and applications

For a given metrizable space X, we study continuity properties of the entropy as function not only of the measure but also of the dynamical system on X. We introduce the notion of robust tail entropy, which implies upper semicontinuity of the topological entropy but also stability of measures of maximal entropy (when the topological entropy is continuous). This gives, inter alia, simple proofs of results of Misiurewicz and Raith for multimodal interval maps. We also consider fibred entropy structures which allow us to investigate the symbolic extensions of (smooth) skew-product systems.