Mean and cell-mean imputation resulting in the use of a semicontinuous distribution and a mixture of semicontinuous distributions

Zero-inflated models are commonly used for modeling count and continuous data with extra zeros. Inflations at one point or two points apart from zero for modeling continuous data have been discussed less than that of zero inflation. In this article, inflation at an arbitrary point α as a semicontinuous distribution is presented and the mean imputation for a continuous response is discussed as a cause of having semicontinuous data. Also, inflation at two points and generally at k arbitrary points and their relation to cell-mean imputation in the mixture of continuous distributions are studied. To analyze the imputed data, a mixture of semicontinuous distributions is used. The effects of covariates on the dependent variable in a mixture of k semicontinuous distributions with inflation at k points are also investigated. In order to find the parameter estimates, the method of expectation–maximization (EM) algorithm is used. In a real data of Iranian Households Income and Expenditure Survey (IHIES), it is shown how to obtain a proper estimate of the population variance when continuous missing at random responses are mean imputed.     

Economic and economic-statistical designs of the T2 control charts with SVSSI sampling scheme

Recent studies have shown that the adaptive T² chart with two different sampling interval and three sample sizes (SVSSI) shows a good performance in detecting small to large shifts in the process mean. This paper investigates the economic and economic statistical designs of the SVSSI T² charts. We use the Markov chain approach to developing the cost model proposed by Costa and Rahim (Journal of applied statistics 2001; 28: 875–885). A genetic algorithm approach is used to find the optimal solutions. Using numerical examples, we illustrate the performance of the proposed model and compare the statistical, economic, and economic statistical designs of the SVSSI T² chart with respect to the economic and statistical criteria. Furthermore, we compare the performance of the SVSSI T² chart with the other T² control schemes.     

大样本线性回归模型的子抽样及变量选择

文章考虑了大样本下线性回归中同时进行快速估计和变量选择的问题,即针对一个存在稀疏解的大样本线性模型,根据重要性抽样分布从全数据集抽取少量子样本,对该子样本进行自适应Lasso估计。通过随机模拟研究,将该算法分别应用在几种不同的数据集中,并从模型预测精度和可解释性两个方面比较了四种子抽样方法在该算法下的表现。模拟结果表明,所提出的算法具有良好表现,在计算开销上也具有一定优势。     

R routines for performing estimation and statistical process control under copula-based time series models

Modeling serial dependence in time series is an important step in statistical process control. We provide a set of automatic routines useful for simulating and analyzing time series under a copula-based serial dependence. First, we introduce routines that generate time series data under a given copula. Second, we provide fully automated routines for obtaining maximum likelihood estimates for given time series data and then drawing a Shewhart-type control chart. Finally, real data are analyzed for illustration. We make the routines available as “Copula.Markov” package in R.     

Analysis of Gamma and Weibull lifetime data under a general censoring scheme and in the presence of covariates

We consider the problem of estimating the lifetime distributions of survival times subject to a general censoring scheme called “middle censoring”. The lifetimes are assumed to follow a parametric family of distributions, such as the Gamma or Weibull distributions, and is applied to cases when the lifetimes come with covariates affecting them. For any individual in the sample, there is an independent, random, censoring interval. We will observe the actual lifetime if the lifetime falls outside of this censoring interval, otherwise we only observe the interval of censoring. This censoring mechanism, which includes both right- and left-censoring, has been called “middle censoring” (see Jammalamadaka and Mangalam, 2003 Jammalamadaka, S. Rao, Mangalam, V. (2003). Nonparametric estimation for middle censored data. J. Nonparamet. Stat. 15(2):253–265.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Maximum-likelihood estimation of the parameters as well as their large-sample properties are studied under this censoring scheme, including the case when covariates are available. We conclude with an application to a dataset from Environmental Economics dealing with ContingentValuation of natural resources.     

并行支路卷积碼譯碼算法之簡化

本文主要工作:(1)从算法上证明了对于TCM用的一类有并行转移支路的卷积玛,其VITERBI译码算法可以作很有效的简化,而且简化不影响译码性能。(2)给出了表征算法复杂性的公式,讨论了简化效果。简化算法的复杂性约为常规算法的1/2~P。(3)用计算机模拟验证了简化算法和常规算法的等效性。因而,算法的简化不影响译码性能。     

神经网络用于精确预言含F铋系高温超导的T_C

本工作通过选取适当的拓朴结构,特别是对零电阻转变温度 T_c 的数据采用压缩与还原的技术处理,建立了一个基于误差反向传播(简记为 B-P)算法的人工神经网络。该网络可根据制备含 F 铋系高温超导材料的几个主要实验参数,相当精确地预言此类超导体的 T_c。本方法显然具有一般意义,从而有力地促进了材料的计算机辅助设计的研究工作,使之进一步趋于成熟。     

Study on Multi-Trip Vehicle Routing Problem with Stochastic Simultaneous Pickup-Delivery Demand

Characterized by high service efficiency and low vehicle cost, multi-trip distribution allows the vehicle to travel multiple times between the distribution center and customers in order to complete the delivery task. Dynamic and uncertain customer demand is common during delivery. Combined pickup-delivery integration pattern with stochastic customer demand, an optimization model for multi-trip vehicle routing problem is set up with stochastic simultaneous pickup-delivery demand. Due to the sudden and simultaneous pickup-delivery of dynamic demand, the original distribution scheme needs to be optimized and adjusted, and the multi-trip routing adjustment strategy of real-time flexible point is proposed. According to the stochastic characteristics of the optimization model, a stochastic chance-constrained programming transformation model is introduced. A hybrid algorithm with nested stochastic simulation and variable neighborhood tabu search algorithm is designed to find the optimal distribution path. Finally, an example shows that the optimization model and algorithm are feasible and effective.     

A new model selection procedure for finite mixture regression models

Abstract

In this article, we propose a new penalized-likelihood method to conduct model selection for finite mixture of regression models. The penalties are imposed on mixing proportions and regression coefficients, and hence order selection of the mixture and the variable selection in each component can be simultaneously conducted. The consistency of order selection and the consistency of variable selection are investigated. A modified EM algorithm is proposed to maximize the penalized log-likelihood function. Numerical simulations are conducted to demonstrate the finite sample performance of the estimation procedure. The proposed methodology is further illustrated via real data analysis.     

从人工智能、基因技术到人性危机:对科技宗教的先验现象学反思

近年来人工智能、认知科学和基因技术的突破,导致一种科技宗教的产生.这种自然主义、物理主义的意识形态,把生命和意识归约为算法加数据流,从而导致人性的危机和人文主义的破产.从先验现象学的进路分析意识难题和物理主义,可以基于先验主体性思路,给这些科学以另一种解读,同时重新阐释人性和人文主义,以消除科学危机和人类精神的危机.