Confidence intervals for secondary parameters following a sequential test

In sequential studies, formal interim analyses are usually restricted to a consideration of a single null hypothesis concerning a single parameter of interest. Valid frequentist methods of hypothesis testing and of point and interval estimation for the primary parameter have already been devised for use at the end of such a study. However, the completed data set may warrant a more detailed analysis, involving the estimation of parameters corresponding to effects that were not used to determine when to stop, and yet correlated with those that were. This paper describes methods for setting confidence intervals for secondary parameters in a way which provides the correct coverage probability in repeated frequentist realizations of the sequential design used. The method assumes that information accumulates on the primary and secondary parameters at proportional rates. This requirement will be valid in many potential applications, but only in limited situations in survival analysis.     

Analysis of Competing Risks by Using Bayesian Smoothing

We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end.     

GMM Estimation with persistent panel data: an application to production functions

This paper considers the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. GMM estimatorshave been found to produce large finite-sample biases when using the standard first-differenced estimator. These biases can be dramatically reduced by exploiting reasonable stationarity restrictions on the initial conditions process. Using data for a panel of R&Dperforming US manufacturing companies we find that the additional instruments used in our extended GMM estimator yield much more reasonable parameter estimates.     

论高校专业资料室在教改中的特点及服务

本文从多方面对高校专业资料室的工作特点、内容及形式进行探讨 ,以使其在教改及科学研究中发挥更大的作用。     

《古小说钩沉》中的民间佛教及其史料研究

鲁迅所辑的《古小说钩沉》主要涉及三国到南北朝时期的志怪小说,历来为文学界所重视,并作为中国小说发展史史料而被广泛引用。在民间佛教这一尚待开拓的研究领域,《古小说钩沉》为两晋南北朝的民间佛教研究提供了重要的史料。《古小说钩沉》的民间佛教突出灵验特色,在信仰、仪式、象征体系中形成了自己的特点。中国民间佛教的定型主要是在南北朝时期完成的。     

Binary Dynamic Logit for Correlated Ordinal: estimation,application and simulation

We evaluate the estimation performance of the Binary Dynamic Logit model for correlated ordinal variables (BDLCO model), and compare it to GEE and Ordinal Logistic Regression performance in terms of bias and Mean Absolute Percentage Error (MAPE) via Monte Carlo simulation. Our results indicate that when the proportional-odds assumption does not hold, the proposed BDLCO method is superior to existing models in estimating correlated ordinal data. Moreover, this method is flexible in terms of modeling dependence and allows unequal slopes for each category, and can be used to estimate an apple bloom data set where the proportional-odds assumption is violated. We also provide a function in R to implement BDLCO.     

A multivariate zero-inflated binomial model for the analysis of correlated proportional data

In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.     

数字化转型背景下油气开发生产组织新模式探索

智能化是强国战略的必备基础,也是企业转型升级的方向。随着大数据、云计算、人工智能和工业互联网等新兴技术蓬勃兴起,全球石油公司不断通过数字化转型以提高核心竞争力。智能化油气开发生产具有全面感知、自动操控、趋势预测、优化决策的特征。目前,我国油气开发生产数字化转型面临四个困境：各油气田硬件基础设施参差不齐、数据共享与业务协同处于较低水平、传统的油气开发生产模式不适应智能化条件、智能化油气田建设运行尚无统一标准可供借鉴。智能化油气田开发生产组织新模式应以业务协作共享、一体化智能协同为主要特色,积极实现操作巡检维护、开发生产管理、协同研究、经营管理与决策的一体化智能运营。     

电子商务的智能化营销

互联网和电子商务的出现,在信息获得的手段和方法上突破了传统时空观念的限制,在多方面动摇了企业经营和营销理论的根基。同时也改变了企业外部经营和购销的实务操作模式。传统行业的电子商务得到了广泛的应用,积累了大量的数据,在强大的数据库技术和数据挖掘工具的支持下,从海量的数据中挖掘出有用的信息来提高企业经营管理水平已成为热门的课题,为企业实现智能化营销提供了坚实的基础。     

数据挖掘中关联规则的规范化描述

一、引言数据挖掘(Data Mining)是近年来随着人工智能和数据库技术的发展而出现的一门新兴学科。它是从大量的数据中筛选出隐含的、可信的、新颖的、有效的信息的高级处理过程。关联规则(Association Rule)是其中重要的研究课题,是数据挖掘的主要技术之一,也是在无指导学习系统