Recent zbMATH articles in MSC 62P30https://zbmath.org/atom/cc/62P302024-03-13T18:33:02.981707ZWerkzeugFrame-type kernel and time-frequency transformshttps://zbmath.org/1528.420112024-03-13T18:33:02.981707Z"Chen, Qiuhui"https://zbmath.org/authors/?q=ai:chen.qiuhui"Zhang, Yiqiao"https://zbmath.org/authors/?q=ai:zhang.yiqiao(no abstract)Poisson generalized Lindley process and its propertieshttps://zbmath.org/1528.600872024-03-13T18:33:02.981707Z"Cha, Ji Hwan"https://zbmath.org/authors/?q=ai:cha.ji-hwan"BadÃa, F. G."https://zbmath.org/authors/?q=ai:badia.francisco-germanSummary: In spite of the practical usefulness of the nonhomogeneous Poisson process, it still has some restrictions. To overcome these restrictions, the Poisson Lindley process has been recently developed and introduced in
[\textit{J. H. Cha}, Stat. Probab. Lett. 152, 74--81 (2019; Zbl 1451.60050)]. In this paper, we further generalize the Poisson Lindley process, so that the developed counting process model should have the restarting property and it should include the generalized Polya process as a special case. Some basic stochastic properties of the developed counting process model are derived. Dependence properties and stochastic comparisons are also discussed under a more general framework.Unification of some multivariate process capability indices for asymmetric specification regionhttps://zbmath.org/1528.620852024-03-13T18:33:02.981707Z"Chatterjee, Moutushi"https://zbmath.org/authors/?q=ai:chatterjee.moutushi"Chakraborty, Ashis Kumar"https://zbmath.org/authors/?q=ai:chakraborty.ashis-kumarSummary: In manufacturing industries, it is often seen that the bilateral specification limits corresponding to a particular quality characteristic are not symmetric with respect to the stipulated target. A unified superstructure \(C_p^{\prime\prime}(u, v)\) of univariate process capability indices was specially designed for processes with asymmetric specification limits. However, as in most of the practical situations a process consists of a number of inter-related quality characteristics, subsequently, a multivariate analogue of \(C_p^{\prime\prime}(u, v)\), which is called \(C_M(u, v)\), was developed. In the present paper, we study some properties of \(C_M(u, v)\) like threshold value and compatibility with the asymmetry in loss function. We also discuss estimation procedures for plug-in estimators of some of the member indices of \(C_M(u, v)\). Finally, the superstructure is applied to a numerical example to supplement the theory developed in this article.
{{\copyright} 2017 The Authors. Statistica Neerlandica {\copyright} 2017 VVS.}Regional differences of high-quality development level for manufacturing industry in Chinahttps://zbmath.org/1528.620862024-03-13T18:33:02.981707Z"Han, Zhi-Ying"https://zbmath.org/authors/?q=ai:han.zhi-ying"Liu, Yong"https://zbmath.org/authors/?q=ai:liu.yong.15"Guo, Xue-ge"https://zbmath.org/authors/?q=ai:guo.xue-ge"Xu, Jun-qian"https://zbmath.org/authors/?q=ai:xu.jun-qianSummary: The development of China's manufacturing industry is still facing the challenge of regional imbalance. To solve the problem of development imbalance, it is necessary to realize regional development. First, we must analyze the development characteristics of different regions. To this end, we consider the requirements of the new development era and design an evaluation index system for the high-quality development level of the manufacturing industry from the dimensions of innovation, green, and efficiency. Then construct a novel hybrid model which combines the grey incidence clustering model and AP algorithm for panel data in this paper. According to the statistical data from 2014 to 2018, we find out the high-quality development of China's manufacturing industry is characterized by obvious regional differences, different development stages and different constraints.Cusums for tracking arbitrary functionalshttps://zbmath.org/1528.620872024-03-13T18:33:02.981707Z"Withers, Christopher S."https://zbmath.org/authors/?q=ai:withers.christopher-s"Nadarajah, Saralees"https://zbmath.org/authors/?q=ai:nadarajah.saraleesSummary: Cusum charts are widely used for detecting deviations of a process about a target value and also for finding evidence of change in the mean of a process. The testing theory approximates the process by a Wiener process or a Brownian bridge, respectively. For quality control, it is important that other aspects are monitored in addition to or instead of the mean. Here, we show that cusum theory is easily adapted when the target is not the mean but some other aspect of the distribution.
{{\copyright} 2016 The Authors. Statistica Neerlandica {\copyright} 2016 VVS.}