A class of regression models for parallel and series systems with a random number of components

We extend the Weibull power series (WPS) class of distributions to the new class of extended Weibull power series (EWPS) class of distributions. The EWPS distributions are related to series and parallel systems with a random number of components, whereas the WPS distributions [Morais AL, Barreto-Souza W. A compound class of Weibull and power series distributions. Computational Statistics and Data Analysis. 2011;55:1410–1425] are related to series systems only. Unlike the WPS distributions, for which the Weibull is a limiting special case, the Weibull law is a particular case of the EWPS distributions. We prove that the distributions in this class are identifiable under a simple assumption. We also prove stochastic and hazard rate order results and highlight that the shapes of the EWPS distributions are markedly more flexible than the shapes of the WPS distributions. We define a regression model for the EWPS response random variable to model a scale parameter and its quantiles. We present the maximum likelihood estimator and prove its consistency and asymptotic normal distribution. Although series and parallel systems motivated the construction of this class, the EWPS distributions are suitable for modelling a wide range of positive data sets. To illustrate potential uses of this model, we apply it to a real data set on the tensile strength of coconut fibres and present a simple device for diagnostic purposes.     

Checking identifiability of covariance parameters in linear mixed effects models

To build a linear mixed effects model, one needs to specify the random effects and often the associated parametrized covariance matrix structure. Inappropriate specification of the structures can result in the covariance parameters of the model not identifiable. Non-identifiability can result in extraordinary wide confidence intervals, and unreliable parameter inference. Sometimes software produces implication of model non-identifiability, but not always. In the simulation of fitting non-identifiable models we tried, about half of the times the software output did not look abnormal. We derive necessary and sufficient conditions of covariance parameters identifiability which does not require any prior model fitting. The results are easy to implement and are applicable to commonly used covariance matrix structures.     

Simulation-based model comparison methodology with application to road accident models

This article assumes the goal of proposing a simulation-based theoretical model comparison methodology with application to two time series road accident models. The model comparison exercise helps to quantify the main differences and similarities between the two models and comprises of three main stages: (1) simulation of time series through a true model with predefined properties; (2) estimation of the alternative model using the simulated data; (3) sensitivity analysis to quantify the effect of changes in the true model parameters on alternative model parameter estimates through analysis of variance, ANOVA. The proposed methodology is applied to two time series road accident models: UCM (unobserved components model) and DRAG (Demand for Road Use, Accidents and their Severity). Assuming that the real data-generating process is the UCM, new datasets approximating the road accident data are generated, and DRAG models are estimated using the simulated data. Since these two methodologies are usually assumed to be equivalent, in a sense that both models accurately capture the true effects of the regressors, we are specifically addressing the modeling of the stochastic trend, through the alternative model. Stochastic trend is the time-varying component and is one of the crucial factors in time series road accident data. Theoretically, it can be easily modeled through UCM, given its modeling properties. However, properly capturing the effect of a non-stationary component such as stochastic trend in a stationary explanatory model such as DRAG is challenging. After obtaining the parameter estimates of the alternative model (DRAG), the estimates of both true and alternative models are compared and the differences are quantified through experimental design and ANOVA techniques. It is observed that the effects of the explanatory variables used in the UCM simulation are only partially captured by the respective DRAG coefficients. This a priori, could be due to multicollinearity but the results of both simulation of UCM data and estimating of DRAG models reveal that there is no significant static correlation among regressors. Moreover, in fact, using ANOVA, it is determined that this regression coefficient estimation bias is caused by the presence of the stochastic trend present in the simulated data. Thus, the results of the methodological development suggest that the stochastic component present in the data should be treated accordingly through a preliminary, exploratory data analysis.     

A comparison of stepwise common singular spectrum analysis and horizontal multi-channel singular spectrum analysis

The problem of forecasting a time series by using information provided by a second time series is considered. Two multivariate extensions of Singular Spectrum Analysis (SSA) are compared in terms of forecast error: Horizontal Multi-channel SSA and Stepwise Common SSA. Different signal structures, defined in terms of trend, period, amplitude and phase, are investigated. In broad terms we find that neither Horizontal Multichannel SSA nor Stepwise Common SSA is best in all cases. Horizontal MSSA is outperformed particularly in cases where different trends are considered.     

Normal mixture method for stock daily returns over different sub-periods

In this paper, the normal mixture model, as an alternative distribution, is utilized to represent the characteristics of stock daily returns over different bull and bear markets. Firstly, we conduct the normality test for the return data and compare the Kolmogorov-Smirnov statistics of normal mixture models with different components. Secondly, we analyze the likely reasons why parameters change over different sub-periods. Our empirical examination proves that majority of the data series reject the normality assumption and mixture models with three components can model the behavior of daily returns more appropriately and steadily. This result has both statistical and economic significance.     

基于稀疏主成分的能源利用效率综合评价及影响因素分析

基于能源消耗水平、能源高效利用水平、污染排放水平和污染治理水平四大准则层构建了评价能源利用效率的指标体系,使用数据包络分析、稀疏主成分方法和熵值法得到了我国各省市自治区2008-2018年的能源利用效率综合评价结果。在此基础上通过构建省际动态面板模型研究了能源利用效率的影响因素,回归结果显示：经济开放性、人均GDP、第三产业占比对能源利用效率有显著的正影响,能源消费结构、政府环境规制对能源利用效率有显著的负影响。最后就如何提高能源利用效率提出了相应的政策建议。     

Early inference on reliability of upgraded automotive components by using past data and technical information

When a new product is the result of design and/or process improvements introduced in its predecessors, then the past failure data and the expert technical knowledge constitute a valuable source of information that can lead to a more accurate reliability estimate of the upgraded product. This paper proposes a Bayesian procedure to formalize the prior information available about the failure probability of an upgraded automotive component. The elicitation process makes use of the failure data of the past product, the designer information on the effectiveness of planned design/process modifications, information on actual working conditions of the upgraded component and, for outsourced components, technical knowledge on the effect of possible cost reductions. By using the proposed procedure, more accurate estimates of the failure probability can arise. The number of failed items in a future population of vehicles is also predicted to measure the effect of a possible extension of the warranty period. Finally, the proposed procedure was applied to a case study and its feasibility in supporting reliability estimation is illustrated.     

关于人的全面发展的辩证思考

人的全面发展有着丰富的内涵,它是个人发展与人类整体发展的统一;物质生活发展与思想和精神生活 发展的统一;人的社会化与个性化的统一;理想与现实的统一;目标与过程的统一;目的与手段的统一。     

A functional data-analytic approach to the classification of species according to their spatial dispersion. Application to a marine macrobenthic community from the Bay of Morlaix (Western English Channel)

We investigate with multivariate methods the behaviour of species collected in a sequence of ecological surveys. The behaviour of the sth species is first characterized by a classical dispersion index. Under the hypothesis (H) of spatial randomness, the probability distribution v _s of this index obeys a reference law μ. All the sampled species are then compared through a Principal Components Analysis whose metric structure depends on μ. More precisely, the distance between two species s and s′ is an approximation of the μ-centred chi-square distance (Benzécri 1976 Benzécri, J. P. 1976. L'Analyse des Données, tome 2: L'Analyse des Correspondances, Paris: Dunod.  [Google Scholar]) between v _s and v _s′. Thus, while Correspondence Analysis displays departures from independence or homogeneity, the proposed analysis displays departures of the species from (H). As an application, a macrobenthic data time series is analysed, and the obtained species typology is described and discussed. The method enabled us to separate rare species from random ones while rare species could easily be confused with random ones. All the aggregated species were common (or even dominant), and most random ones were moderately abundant. Finally, a group of 23 species showed a mixed random-aggregated behaviour. The repulsive (uniform) behaviour was extremely rare.     

利用MATLAB软件处理和分析机械零件图像

本文简要介绍了MATLAB软件特点,利用MATLAB软件图像处理工具箱中的函数对机械零件图 像进行处理和分析,并给出了试验结果。