A Two-Step Algorithm to Estimate Variable Importance for Multi-State Data:An Application to COVID-19

Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events.One main objective in the MSM framework is variable selection,where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression.The usual variable selection methods,including stepwise and penalized methods,do not provide information about the importance of variables.In this context,we present a two-step algorithm to evaluate the importance of variables formulti-state data.Three differentmachine learning approaches(randomforest,gradient boosting,and neural network)as themost widely usedmethods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance.The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set.The results revealed that the proposed two-stage method has promising performance for estimating variable importance.

脆弱性模型的随机效应检验和推广的拟合检验

目的本文在脆弱性模型框架下推广了Cox-Snell残差和Deviance残差,并且利用这两种残差图对肾病感染数据和烧伤病人皮肤移植数据的脆弱性模型进行拟合检验。方法本文根据等级似然估计理论,建立了在脆弱性模型下检验随机效应的WALD检验和LR检验,并把这两个检验与Commenges和Anderson(1995)的得分(SCORE)检验一起做模拟比较研究。结果发现威布尔脆弱性模型下WALD检验和LR检验功效都很高,且优于得分检验。对肾病感染数据和烧伤病人皮肤移植数据建立脆弱性模型估计并进行检验,发现确实存在个体异质性。结论本文在脆弱性模型框架下推广了Cox-Snell残差和Deviance残差,发现这两种残差用于脆弱性模型的拟合检验是合适的。

Impulsivity Subtypes and Maladaptive Road Performance among Drivers in Germany and Switzerland

Excessive speed and speeding substantially compromise road safety in Germany and Switzerland.Approximately one third of all fatal accidents are caused by maladjusted speed.Recent studies attribute a special importance to the impulsivity construct in the context of maladaptive road behavior.Thus,the effects of impulsivity on risky driving behaviors(speeding violations)were examined in a Swiss-German sample of N=361 car drivers(both on speed affine drivers and putative ordinary drivers).The participants filled in a questionnaire battery consisting of an impulsiveness scale as well as traffic-related attitudes and cognitive appraisal tendencies on the one hand and indicators for maladaptive behaviors at and beyond traffic domain on the other hand.The directions of the observed correlations between the scales were as expected,with impulsivity correlating negatively with age(young drivers scored higher)but not at all with gender or driving experience.To find out more about the functionality of impulsivity,specific personality profiles were carried out via cluster analysis.Three different control types were empirically found(impulsivity subtype,reduced compliance subtype,vulnerability subtype),while high impulsive drivers scored high in impulsivity,low on compliance,high on affective responsiveness and described themselves as affordance-prone.The impulsive type additionally shows more speeding offences stored in the driving license file,overrides speed limits for more than 15 km/h more frequently and even shows deviancy beyond traffic domain.The results are discussed in the light of the impulse control system and conclusions are drawn regarding assessment of driving aptitude and interventions.The theoretical framework including a hierarchical structured model of deviance was confirmed empirically.

Variable selection-based SPC procedures for high-dimensional multistage processes

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.

Tattoos as a window to the psyche:How talking about skin art can inform psychiatric practice

Tattooing the skin as a means of personal expression is a ritualized practice that has been around for centuries across many different cultures.Accordingly,the symbolic meaning of tattoos has evolved over time and is highly individualized,from both the internal perspective of the wearer and the external perspective of an observer.Within modern Western societies through the 1970 s,tattoos represented a cultural taboo,typically associated with those outside of the mainstream such as soldiers,incarcerated criminals,gang members,and others belonging to marginalized and counter-cultural groups.This paper aims to review the more recent epidemiology of tattoos in Western culture in order to establish that tattooing has become a mainstream phenomenon.We then review psychological and psychiatric aspects of tattoos,with a goal of revising outmoded stigmas about tattooing and helping clinicians working with tattooed patients to facilitate an exploration of the personal meaning of skin art and self-identity.We suggest that as a kind of augmentation of the physical exam,looking at and talking to patients about their tattoos can provide a valuable window into the psyche,informing clinical practice.

Strategic Deviance,Customer Concentration and Enterprise Value——A Case Study of Agricultural Listed Enterprises

From the perspective of strategy,this paper uses A-share agricultural listed companies( 2011-2016) as samples to study the impact of strategic deviance on enterprise value,to explore whether customer concentration plays a mediating effect. The results show that customer concentration plays a mediating effect in the relationship between strategic deviance and enterprise value. The strategic deviance is positively correlated with customer concentration and negatively correlated with enterprise value. The management of agricultural enterprises must pay attention to the customer relationship problems caused by strategic deviance,reduce the risk of enterprises,and gradually enhance the value of enterprises.

Spatial Modeling and Mapping of Tuberculosis Using Bayesian Hierarchical Approaches

Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes. The use of frequentist methods in biostatistics and epidemiology is common and is therefore extensively utilized in answering varied research questions. In this paper, we applied the hierarchical Bayesian approach to study the spatial distribution of tuberculosis in Kenya. The focus was to identify best fitting model for modeling TB relative risk in Kenya. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages was used for simulations. The Deviance Information Criterion (DIC) proposed by [1] was used for models comparison and selection. Among the models considered, unstructured heterogeneity model perfumes better in terms of modeling and mapping TB RR in Kenya. Variation in TB risk is observed among Kenya counties and clustering among counties with high TB Relative Risk (RR). HIV prevalence is identified as the dominant determinant of TB. We find clustering and heterogeneity of risk among high rate counties. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial methodology for the statistical analysis of risk from TB in Kenya.

INFLUENCE ANALYSIS OF LINK PARAMETER IN GLM

in this paper, we describe a new method for assessing the degree in which the individual case influence the maximum likelihood estimation of link parameter in generalized linear model. Several influential statistics are illustrated with two examples.

Investigating Spatio-Temporal Pattern of Relative Risk of Tuberculosis in Kenya Using Bayesian Hierarchical Approaches

Proper understanding of global distribution of infectious diseases is an important part of disease management and policy making. However, data are subject to complexities caused by heterogeneities across host classes and space-time epidemic processes. This paper seeks to suggest or propose Bayesian spatio-temporal model for modeling and mapping tuberculosis relative risks in space and time as well identify risks factors associated with the tuberculosis and counties in Kenya with high tuberculosis relative risks. In this paper, we used spatio-temporal Bayesian hierarchical models to study the pattern of tuberculosis relative risks in Kenya. The Markov Chain Monte Carlo method via WinBUGS and R packages were used for simulations and estimation of the parameter estimates. The best fitting model is selected using the Deviance Information Criterion proposed by Spiegelhalter and colleagues. Among the spatio-temporal models used, the Knorr-Held model with space-time interaction type III and IV fit the data well but type IV appears better than type III. Variation in tuberculosis risk is observed among Kenya counties and clustering among counties with high tuberculosis relative risks. The prevalence of HIV is identified as the determinant of TB. We found clustering and heterogeneity of TB risk among high rate counties and the overall tuberculosis risk is slightly decreasing from 2002-2009. We proposed that the Knorr-Held model with interaction type IV should be used to model and map Kenyan tuberculosis relative risks. Interaction of TB relative risk in space and time increases among rural counties that share boundaries with urban counties with high tuberculosis risk. This is due to the ability of models to borrow strength from neighboring counties, such that nearby counties have similar risk. Although the approaches are less than ideal, we hope that our study provide a useful stepping stone in the development of spatial and spatio-temporal methodology for the statistical analysis of risk from tuberculosis in Kenya.

Religious and Identity Trajectories Among the Ebrie of Anono and Blockhaus(Ivory Coast)

In the villages of Anono and Blockhaus,inserted in the district of Abidjan,three religious communities,notably Harrist,Catholic,and Methodist are socially accepted and recognized in the village space.These churches are adopted because they allow religious,cultural,and Christianized practices to coexist within their religious spaces.Social rules and sanctions operate there as a control mechanism for their sustainability.However,several faithful of these local churches are swarming for the benefit of the so-called“Evangelical”churches.This break is regarded by the collective memory as an act of deviance which involves stigma.We try to explore in what follows,20 biographical journeys of the faithful who abandon locally recognized churches.It is from the life story,the main data collection tool that we will first demonstrate the social situations of their affiliation to these churches(past experiences).Secondly,we will describe the social conditions of their disaffiliation(lived experiences)and thirdly,we will identify identity reconstruction strategies(experiences to be lived).They constitute an adaptive response to the process of social stigmatization maintained by the local chiefdom and large families.

A New Approach for Dispersion Parameters

This paper presents a new approach to identify and estimate the dispersion parameters for bivariate, trivariate and multivariate correlated binary data, not only with scalar value but also with matrix values. For this direction, we present some recent studies indicating the impact of over-dispersion on the univariate data analysis and comparing a new approach with these studies. Following the property of McCullagh and Nelder [1] for identifying dispersion parameter in univariate case, we extended this property to analyze the correlated binary data in higher cases. Finally, we used these estimates to modify the correlated binary data, to decrease its over-dispersion, using the Hunua Ranges data as an ecology problem.

Stochastic Volatility Modeling based on Doubly Truncated Cauchy Distribution and Bayesian Estimation for Chinese Stock Market

In order to measure the uncertainty of financial asset returns in the stock market, this paper presents a new model, called SV-dt C model, a stochastic volatility(SV) model assuming that the stock return has a doubly truncated Cauchy distribution, which takes into account the high peak and fat tail of the empirical distribution simultaneously. Under the Bayesian framework, a prior and posterior analysis for the parameters is made and Markov Chain Monte Carlo(MCMC) is used for computing the posterior estimates of the model parameters and forecasting in the empirical application of Shanghai Stock Exchange Composite Index(SSECI) with respect to the proposed SV-dt C model and two classic SV-N(SV model with Normal distribution)and SV-T(SV model with Student-t distribution) models. The empirical analysis shows that the proposed SV-dt C model has better performance by model checking, including independence test(Projection correlation test), Kolmogorov-Smirnov test(K-S test) and Q-Q plot. Additionally, deviance information criterion(DIC) also shows that the proposed model has a significant improvement in model fit over the others.

Change-point estimation for censored regression model

In this paper, we consider the change-point estimation in the censored regression model assuming that there exists one change point. A nonparametric estimate of the change-point is proposed and is shown to be strongly consistent. Furthermore, its convergence rate is also obtained.