ANOVA extensions for mixed discrete and continuous data

This paper is concerned with ANOVA-like tests in the context of mixed discrete and continuous data. The likelihood ratio approach is used to obtain a location test in the mixed data setting after specifying a general location model for the joint distribution of the mixed discrete and continuous variables. The approach allows the problem to be treated from a multivariate perspective to simultaneously test both the discrete and continuous parameters of the model, thus avoiding the problem of multiple significance testing. Moreover, associations among variables are accounted for, resulting in improved power performance of the test. Unlike existing distance-based alternatives which rely on asymptotic theory, the likelihood ratio test is exact. In addition, it can be viewed as an extension to the mixed data setting of the classical multivariate ANOVA. We compare its performance against those of currently available tests via Monte Carlo simulations. Two real-data examples are presented to illustrate the methodology.     

Parametric inference for progressive Type-I hybrid censored data on a simple step-stress accelerated life test model

This paper considers a simple step-stress accelerated life test model under progressive Type-I hybrid censoring scheme. The progressive Type-I hybrid censoring scheme and statistical method in synthetic accelerated stresses are provided so as to decrease the lifetime and reduce the test cost. An exponentially distributed life of test units and a cumulative exposure model are assumed. The maximum likelihood estimates of the model parameters are obtained using a pivotal quantity. Two useful lemmas and a theorem are given to construct the approximate confidence intervals for the model parameters. Finally, simulation results are provided to assess the method of inference developed in this article. The simulation results show that the method does improve for large sample size.     

基于Logistic的信用卡套现侦测评分模型

信用卡套现是信用卡产业面临的一种主要风险.单笔的套现交易与正常交易间无显著差异,难以进行基于特征的过滤筛选.为自动、高效地识别套现账户,根据统计学特征先遴选出各相关变量,并结合业务分析,利用Logistic回归模型的非线性曲线特征缺陷,克服其自变量多维相关敏感性缺陷,计算出各变量的影响权重系数,构建了信用卡套现侦测评分模型.实践表明识别准确率达到82.72%.     

A block-diagonal growth curve model

We consider a variation of the growth curve (GC) model, referred to as the block-diagonal growth curve (BDGC) model, where the unknown regression coefficient matrix is constrained to be block-diagonal. A closed-form approximate maximum likelihood (AML) estimator for this model is derived based on the maximum likelihood principle. We analyze the statistical properties of this method theoretically and show that the AML estimate is unbiased and asymptotically statistically efficient for a large snapshot number. Via numerical examples in wireless communications, we also show that the proposed AML estimator can achieve excellent estimation accuracy.