Models and estimation for systems with recurrent events and usage processes

This paper considers settings where populations of units may experience recurrent events, termed failures for convenience, and where the units are subject to varying levels of usage. We provide joint models for the recurrent events and usage processes, which facilitate analysis of their relationship as well as prediction of failures. Data on usage are often incomplete and we show how to implement maximum likelihood estimation in such cases. Random effects models with linear usage processes and gamma usage processes are considered in some detail. Data on automobile warranty claims are used to illustrate the proposed models and estimation methodology.     

Preserving data utility via BART

When preparing data for public release, information organizations face the challenge of preserving the quality of data while protecting the confidentiality of both data subjects and sensitive data attributes. Without knowing what type of analyses will be conducted by data users, it is often hard to alter data without sacrificing data utility. In this paper, we propose a new approach to mitigate this difficulty, which entails using Bayesian additive regression trees (BART), in connection with existing methods for statistical disclosure limitation, to help preserve data utility while meeting confidentiality requirements. We illustrate the performance of our method through both simulation and a data example. The method works well when the targeted relationship underlying the original data is not weak, and the performance appears to be robust to the intensity of alteration.     

Improved likelihood-based inference for the stationary AR(2) model

An improved likelihood-based method based on Fraser et al. (1999) is proposed in this paper to test the significance of the second lag of the stationary AR(2) model. Compared with the test proposed by Fan and Yao (2003) and the signed log-likelihood ratio test, the proposed method has remarkable accuracy. Simulation studies are performed to illustrate the accuracy of the proposed method. Application of the proposed method on historical data is presented to demonstrate the implementation of this method. Furthermore, the method can be extended to the general AR(p) model.     

Random Weighted Bootstrap Method for Recurrent Events with Informative Censoring

Using the data from the AIDS Link to Intravenous Experiences cohort study as an example, an informative censoring model was used to characterize the repeated hospitalization process of a group of patients. Under the informative censoring assumption, the estimators of the baseline rate function and the regression parameters were shown to be related to a latent variable. Hence, it becomes impractical to directly estimate the unknown quantities in the moments of the estimators for the bandwidth selection of a smoothing estimator and the construction of confidence intervals, which are respectively based on the asymptotic mean squared errors and the asymptotic distributions of the estimators. To overcome these difficulties, we develop a random weighted bootstrap procedure to select appropriate bandwidths and to construct approximated confidence intervals. One can see that our method is simple and faster to implement from a practical point of view, and is at least as accurate as other bootstrap methods. In this article, it is shown that the proposed method is useful through the performance of a Monte Carlo simulation. An application of our procedure is also illustrated by a recurrent event sample of intravenous drug users for inpatient cares over time.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

自杀和生命的意义——来自脑科学研究的解读

生命本身是一个随机事件,并不具有高尚的意义。进化的结果使人类可通过工作、家庭和人际关系等给予自己的生命以意义感。当我们的生活方式使脑内奖赏系统获得刺激,就会带来生命具有意义的感受,这种感受给人带来更好的生存机会。生存的欲望是一切生物体最具特征的属性,一些精神疾病患者缺乏这种欲望。自杀主要发生于精神疾病患者,它并非是基于自由意志的行为。抗精神病治疗和适当的社会交往可以预防自杀。由于不同个体有着不同的大脑,人们的生活方式就应该去适应各自大脑的特征。只要人们的生活方式不对他人造成过多的伤害,政府就应该允许和保护人们自由地按照各自的方式生活。     

An empirical investigation of human dyadic systems in the time and frequency domains

This paper presents time and frequency domain analyses of psychotherapy data drawn from a unique dyadic system--the patient/therapist (P/T) system. The data are constituted by a time series defined by which member of the P/T system held the speaker role at each second of ten recorded psychotherapy consultations. The series reflects a specific property of communication within the dyad. Each series significantly followed a first order autoregressive model. That is, each was highly sensitive to the prior state of the system, or equivalently, the system showed feedforward of its prior state. These models became more significant after intervention analysis further clarified the underlying autoregressive structure. The histograms of the frequency of the length of each utterance indicated an underlying Poisson model for the emergence of speech. The parameters of the Poisson and autoregressive models were used to determine blindly which of four pairs of interviews involved the same therapist with two different patients. The method correctly identified three of the therapist four pairs but failed to identify different interviews with the same patient, a significantly negative result. These results suggest that certain time domain measures are therapist-dominated, so that the therapist drives change in these areas within the P/T system more strongly than the patient. In the frequency domain, the coherency between each of the 45 pairs of speaker series was used in an attempt to identify which time series corresponded systematically to the same therapists or patients. This proved futile because no two pairs of series--no P/T system--showed significant non-zero coherency. This result suggests that the harmonics of the switching of the speaker role are not determined by the therapist or patient alone but by the P/T system as an entity. We conclude that human dyadic systems may show predominant influence of one member or jointly determined patterns that are highly distinctive.     

臭氧技术在柴油机中的应用

通过对柴油发动机臭氧助燃机理分析,设计了臭氧发生器,并将其用于6135型柴油发动机台架试验中。试验结果证明：臭氧参与柴油发动机的燃烧过程起到了节油和降低排放的双重效果。