犹太人能成为基督徒吗?反思、挑战与困惑

本文从一名犹太基督徒的视角出发,阐述了在作者身上所发生的犹太教与基督教身份之间的张力,并从耶稣、保罗以及早期犹太基督徒身上寻找解决方案,进而消解基督教与犹太教之间历史上的误会与冲突,以及如何从基督教的角度去协同与犹太教的关系。最后,作者陈述了现代以色列国内巴勒斯坦人的现状,并提出作为一个基督徒的正当立场。     

Minimum Income Standards in Israel: A Recent History in Economic and Humanistic Discourses

Recent years have seen a growing interest in the creation of Minimum Income Standards (MIS). The impetus for this is the belief that governments that do not employ MIS to set income support can ultimately be persuaded to do so. Even as scholars recognize the many hurdles in the way of an MIS's acceptance, they nonetheless hope, and seem to expect, that the hurdles can be overcome and the political will be created to adopt empirical MIS that would allow a decent standard of living. This article, a case study analyzing the setting of income support levels in Israel over more than seven decades, questions this assumption. The analyses, anchored in Veit‐Wilson's notion of discourses, show that the absence of governmental MIS in Israel has not been incidental, but the outcome of repeated conscious decisions, anchored in the primacy of the asocial economistic discourse. This discourse, it shows, served as a strong disincentive to the adoption of governmental MIS, which would have required the government to justify its choice to set income support below that which was needed for a minimum standard of living.     

Assessing discrimination of risk prediction rules in a clustered data setting

The AUC (area under ROC curve) is a commonly used metric to assess discrimination of risk prediction rules; however, standard errors of AUC are usually based on the Mann–Whitney U test that assumes independence of sampling units. For ophthalmologic applications, it is desirable to assess risk prediction rules based on eye-specific outcome variables which are generally highly, but not perfectly correlated in fellow eyes [e.g. progression of individual eyes to age-related macular degeneration (AMD)]. In this article, we use the extended Mann–Whitney U test (Rosner and Glynn, Biometrics 65:188–197, 2009) for the case where subunits within a cluster may have different progression status and assess discrimination of different prediction rules in this setting. Both data analyses based on progression of AMD and simulation studies show reasonable accuracy of this extended Mann–Whitney U test to assess discrimination of eye-specific risk prediction rules.     

A method for combining inference across related nonparametric Bayesian models

Summary.  We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In the nonparametric context, modelling is complicated by the fact that the random quantities over which we define the hierarchy are infinite dimensional. We discuss a formal definition of such a hierarchical model. The approach includes a regression at the level of the nonparametric model. For the special case of Dirichlet process mixtures, we develop a Markov chain Monte Carlo scheme to allow efficient implementation of full posterior inference in the given model.     

PARAMETER ESTIMATION FOR LOW-ORDER AUTO-REGRESSIVE MODELS WITH MISSING VALUES

Longitudinal data analysis in epidemiological settings is complicated by large multiplicities of short time series and the occurrence of missing observations. To handle such difficulties Rosner & Muñoz (1988) developed a weighted non-linear least squares algorithm for estimating parameters for first-order autoregressive (AR1) processes with time-varying covariates. This method proved efficient when compared to complete case procedures. Here that work is extended by (1) introducing a different estimation procedure based on the EM algorithm, and (2) formulating estimation techniques for second-order autoregressive models. The second development is important because some of the intended areas of application (adult pulmonary function decline, childhood blood pressure) have autocorrelation functions which decay more slowly than the geometric rate imposed by an AR1 model. Simulation studies are used to compare the three methodologies (non-linear, EM based and complete case) with respect to bias, efficiency and coverage both in the presence and in the absence of time-varying covariates. Differing degrees and mechanisms of missingness are examined. Preliminary results indicate the non-linear approach to be the method of choice: it has high efficiency and is easily implemented. An illustrative example concerning pulmonary function decline in the Netherlands is analyzed using this method.     

The Choice of Depreciation Method Under Uncertainty*

This study presents a framework for choosing between depreciation methods when future cash flows from operations are not assumed known with certainty but only in probabilistic terms. Specifically, the accelerated depreciation method and the straight-line depreciation method are compared and mathematical conditions are derived for the depreciation method that should be adopted in different circumstances and under different tax systems. It is shown that, contrary to conventional wisdom, the straight-line depreciation method is the preferred method for lowering the company's present value of tax liability in various realistic situations.     

An Empirical Analysis of the Interrelationship between Components of the Social Quality Theoretical Construct

Social quality has been presented as a theory that can explain economic and social progress of the daily lives of a population. The components of social quality include: socio-economic security, social inclusion, social cohesion and social empowerment. The social quality perspective views people as interacting within collective identities that provide the contexts of self-realisation. The paper tests the social quality theory by focusing on the relationship between social inclusion and social cohesion, the notion of social relations, to socio-economic security using the context of the family as a facilitator of self-realisation. Using data from the Israel Social Survey 2003, six indicators of socio-economic security were analysed. There was a small but positive and significant relationship between social inclusion and socio-economic security. We found no relationship between socio-economic security and social cohesion. These findings tend to undermine those aspects of social quality theory which posit close connections between these elements on a conceptual level.

A nonparametric Bayesian model for inference in related longitudinal studies

Summary.  We discuss a method for combining different but related longitudinal studies to improve predictive precision. The motivation is to borrow strength across clinical studies in which the same measurements are collected at different frequencies. Key features of the data are heterogeneous populations and an unbalanced design across three studies of interest. The first two studies are phase I studies with very detailed observations on a relatively small number of patients. The third study is a large phase III study with over 1500 enrolled patients, but with relatively few measurements on each patient. Patients receive different doses of several drugs in the studies, with the phase III study containing significantly less toxic treatments. Thus, the main challenges for the analysis are to accommodate heterogeneous population distributions and to formalize borrowing strength across the studies and across the various treatment levels. We describe a hierarchical extension over suitable semiparametric longitudinal data models to achieve the inferential goal. A nonparametric random-effects model accommodates the heterogeneity of the population of patients. A hierarchical extension allows borrowing strength across different studies and different levels of treatment by introducing dependence across these nonparametric random-effects distributions. Dependence is introduced by building an analysis of variance (ANOVA) like structure over the random-effects distributions for different studies and treatment combinations. Model structure and parameter interpretation are similar to standard ANOVA models. Instead of the unknown normal means as in standard ANOVA models, however, the basic objects of inference are random distributions, namely the unknown population distributions under each study. The analysis is based on a mixture of Dirichlet processes model as the underlying semiparametric model.     

Measurement error correction for the cumulative average model in the survival analysis of nutritional data: application to Nurses’ Health Study

The use of the cumulative average model to investigate the association between disease incidence and repeated measurements of exposures in medical follow-up studies can be dated back to the 1960s (Kahn and Dawber, J Chron Dis 19:611–620, 1966). This model takes advantage of all prior data and thus should provide a statistically more powerful test of disease-exposure associations. Measurement error in covariates is common for medical follow-up studies. Many methods have been proposed to correct for measurement error. To the best of our knowledge, no methods have been proposed yet to correct for measurement error in the cumulative average model. In this article, we propose a regression calibration approach to correct relative risk estimates for measurement error. The approach is illustrated with data from the Nurses’ Health Study relating incident breast cancer between 1980 and 2002 to time-dependent measures of calorie-adjusted saturated fat intake, controlling for total caloric intake, alcohol intake, and baseline age.     

The average area under correlated receiver operating characteristic curves: a nonparametric approach based on generalized two-sample Wilcoxon statistics

It is well known that, when sample observations are independent, the area under the receiver operating characteristic (ROC) curve corresponds to the Wilcoxon statistics if the area is calculated by the trapezoidal rule. Correlated ROC curves arise often in medical research and have been studied by various parametric methods. On the basis of the Mann–Whitney U-statistics for clustered data proposed by Rosner and Grove, we construct an average ROC curve and derive nonparametric methods to estimate the area under the average curve for correlated ROC curves obtained from multiple readers. For the more complicated case where, in addition to multiple readers examining results on the same set of individuals, two or more diagnostic tests are involved, we derive analytic methods to compare the areas under correlated average ROC curves for these diagnostic tests. We demonstrate our methods in an example and compare our results with those obtained by other methods. The nonparametric average ROC curve and the analytic methods that we propose are easy to explain and simple to implement.