Inference on a progressive type I interval-censored truncated normal distribution

In this paper, we consider the problem of making statistical inference for a truncated normal distribution under progressive type I interval censoring. We obtain maximum likelihood estimators of unknown parameters using the expectation-maximization algorithm and in sequel, we also compute corresponding midpoint estimates of parameters. Estimation based on the probability plot method is also considered. Asymptotic confidence intervals of unknown parameters are constructed based on the observed Fisher information matrix. We obtain Bayes estimators of parameters with respect to informative and non-informative prior distributions under squared error and linex loss functions. We compute these estimates using the importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We present a Monte Carlo simulation study to compare the performance of proposed point and interval estimators. Analysis of a real data set is also performed for illustration purposes. Finally, inspection times and optimal censoring plans based on the expected Fisher information matrix are discussed.     

Statistical inference based on generalized Lindley record values

This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.     

宁远平话的入声研究

宁远平话保留了入声调类,但是入声的来源复杂,除了中古清声母入声字今读入声外,少数浊声母入声字和某些舒声字在平话中也读入声,通过历时来源和现时分布的考察,可以发现宁远平话的入声演变是从浊声母和送气声母开始的,主要元音是低元音和复元音的入声演变比较快。入声通过连读变调,官话的影响继续演变。     

对知识型员工培训的博弈分析

由于培训成本的存在以及知识型员工的高度流动性,企业在对知识型员工的培训过程中,存在一种博弈关系。文章将这种博弈关系归结为一种完全信息静态博弈,并在此基础上建立了对知识型员工培训的博弈模型,进行了求解和分析,最后就企业如何有效地对知识型员工进行培训提出了若干建议措施。     

有限理性下的社保基金投资监管进化博弈研究

社保基金的投资监管是复杂的决策问题,无法满足主流博弈论“完全理性”的要求。因此从有限理性角度出发,分别建立了基金经理人之间、社保基金管理机构与基金经理人之间的进化博弈模型,求出模型的复制动态方程,研究不同情况下的进化稳定策略,并得到博弈群体的复制动态相位图。     

新冠肺炎疫情下对完善快递末端配送体系建设的思考——以重庆市为例

自新冠肺炎疫情发生以来,快递业为应急救援物资和人民群众日常基本生活物资运输和寄递服务、畅通经济循环、满足民生需要等方面做出了重要贡献,为疫情防控提供了强有力的支撑;但同时也暴露出了一些问题,集中体现在末端配送体系不健全、末端收投不畅等方面。在对电子商务和快递业发展现状进行分析的基础上,对快递末端配送的主要模式和发展趋势进行了论述,结合重庆市的实际情况,分析了其末端配送存在的不足,并从公共服务平台搭建、智能化无接触式配送构建、乡村快递服务网络完善、快递员队伍建设等方面提出了对策建议,以助推重庆经济社会高质量发展。     

动漫人才培养模式刍论

我国目前已经出现了波澜不惊的动漫产业潮,但该产业的人才问题已成为制约其健康发展的瓶颈,用何种方式输送该产业发展急需的人才资源,已成为动漫产业发展中亟须解决的迫切问题。简而言之,漫画人才的培养,宜实行“漫画基础人才 项目创造引导”的模式。网游人才和动画人才的培养则可以采用社会类和院校类两种培养机制。     

Accurate estimation for extra-Poisson variability assuming random effect models

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.     

A new regression model for bimodal data and applications in agriculture

We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.     

Modified maximum likelihood estimator under the Jones and Faddy's skew t-error distribution for censored regression model

It is well-known that classical Tobit estimator of the parameters of the censored regression (CR) model is inefficient in case of non-normal error terms. In this paper, we propose to use the modified maximum likelihood (MML) estimator under the Jones and Faddy''s skew t-error distribution, which covers a wide range of skew and symmetric distributions, for the CR model. The MML estimators, providing an alternative to the Tobit estimator, are explicitly expressed and they are asymptotically equivalent to the maximum likelihood estimator. A simulation study is conducted to compare the efficiencies of the MML estimators with the classical estimators such as the ordinary least squares, Tobit, censored least absolute deviations and symmetrically trimmed least squares estimators. The results of the simulation study show that the MML estimators work well among the others with respect to the root mean square error criterion for the CR model. A real life example is also provided to show the suitability of the MML methodology.