Mining for trigger events with survival analysis

This paper discusses a new application of data mining, quantifying the importance of responding to trigger events with reactive contacts. Trigger events happen during a customer’s lifecycle and indicate some change in the relationship with the company. If detected early, the company can respond to the problem and retain the customer; otherwise the customer may switch to another company. It is usually easy to identify many potential trigger events. What is needed is a way of prioritizing which events demand interventions. We conceptualize the trigger event problem and show how survival analysis can be used to quantify the importance of addressing various trigger events. The method is illustrated on four real data sets from different industries and countries.     

低剂量X射线照射诱导HeLa细胞存活的

采用集落形成法观察了低剂量X射线照射HeLa细胞的存活率，结果表明，小于0．5Gy的X射线照射细胞的存活率高于对照，得到了HeLa细胞存活的“兴奋效应”，而且在0．25Gy附近这种效应更为明显；低剂量D1（0．05，0．75Gy)的预照射降低了随后攻击量D2对细胞的损伤程度，反映出低剂量照射可诱发细胞存活的“适应性反应”。     

Bayesian bivariate generalized Lindley model for survival data with a cure fraction

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie–Gumbel–Morgenstern (FGM), Clayton and Gumbel–Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.     

精制胸腺活性肽药效学研究

目的 考察精制胸腺活性肽免疫学活性。方法 建立BALB/c小鼠肝细胞肿瘤(腹水型)模型,经腹腔连续注射肝肿瘤细胞15d,检测各种生命指标,以观察抗肿瘤效果。同时用E-玫瑰花环实验及淋巴细胞转化实验评价其生物学活性。结果 抗肿瘤实验表明,生命各项指标均优于盐水对照组,用药组10d、15d的存活率明显高于对照组;E-玫瑰花环及淋巴细胞转化实验均显示精制胸腺活性肽组明显高于对照组。结论 精制胸腺活性肽具有明显的免疫增强作用。     

遗传算法在试题组卷中的应用与研究

相信大家对达尔文的进化论并不陌生,其中的基因遗传,基因突变等等对基因学得发展起了不可估量的作用。优胜劣汰,即优的基因大量被遗传复制,劣的基因较少被遗传复制。借用这些理论思路,我们可以将其思想用到自动组卷中。所谓自动组卷,即在各种方面不同难度不同题型的题库中选择一套试卷出来,这套试卷对平均难度,总体量,题型比例,重点章节所占试卷比例等等有一定的要求。这样我们就需要一种算法来满足组卷的各方面需求,即遗传算法。本文我们就对遗传算法在组卷中的应用做详细的介绍。     

生存模糊自适应的蚁群算法及收敛性

首次将多元生存分析引入进化算法,设计了一种生存自适应的蚁群算法．对蚁群算法收敛过程建立生存模型,用ＫａｐｌａｎＭｅｉｅｒ法计算生存时间估计值和生存函数曲线,以算法各参数作为生存时间的协变量,用ＣＯＸ 比例危险率回归模型来定量分析其相互依赖关系,分析了种群大小对早熟收敛的影响．根据个体适应度和种群多样性对剩余生存时间进行模糊控制,实现种群规模的自适应调控．数值实例验证了算法的有效性、稳定性及准确性．     

The Poisson-exponential lifetime distribution

In this paper we proposed a new two-parameters lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk problem base. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived analytically in order to obtaining the asymptotic covariance matrix. The methodology is illustrated on a real data set.     

The complementary exponential geometric distribution: Model, properties, and a comparison with its counterpart

In this paper, we proposed a new two-parameter lifetime distribution with increasing failure rate, the complementary exponential geometric distribution, which is complementary to the exponential geometric model proposed by Adamidis and Loukas (1998). The new distribution arises on a latent complementary risks scenario, in which the lifetime associated with a particular risk is not observable; rather, we observe only the maximum lifetime value among all risks. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulas for its reliability and failure rate functions, moments, including the mean and variance, variation coefficient, and modal value. The parameter estimation is based on the usual maximum likelihood approach. We report the results of a misspecification simulation study performed in order to assess the extent of misspecification errors when testing the exponential geometric distribution against our complementary one in the presence of different sample size and censoring percentage. The methodology is illustrated on four real datasets; we also make a comparison between both modeling approaches.     

Survival as a Digital Ghost

You can survive after death in various kinds of artifacts. You can survive in diaries, photographs, sound recordings, and movies. But these artifacts record only superficial features of yourself. We are already close to the construction of programs that partially and approximately replicate entire human lives (by storing their memories and duplicating their personalities). A digital ghost is an artificially intelligent program that knows all about your life. It is an animated auto-biography. It replicates your patterns of belief and desire. You can survive after death in a digital ghost. We discuss a series of digital ghosts over the next 50 years. As time goes by and technology advances, they are progressively more perfect replicas of the lives of their original authors.

Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data

The cure fraction models are usually used to model lifetime time data with long-term survivors. In the present article, we introduce a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates. In order to include the proportion of “cured” patients, mixture and non-mixture formulation models are considered. To demonstrate the ability of using this model in the analysis of real data, we consider an application to data from patients with gastric adenocarcinoma. Inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods.