Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

Benchmark Analysis: Shopping with Proper Confidence

We discuss the issue of using benchmark doses for quantifying (excess) risk associated with exposure to environmental hazards. The paradigm of low-dose risk estimation in dose-response modeling is used as the primary application scenario. Emphasis is placed on making simultaneous inferences on benchmark doses when data are in the form of proportions, although the concepts translate easily to other forms of outcome data.     

对新时期高校政治辅导员的角色认识

从高等学校思想政治工作的现实意义出发,结合目前高校人才培养的新内涵,对政治辅导员的角色进行了定位,即思想政治素质提高的导师;大学生文明修身的引路人;大学生未来职业生涯的设计师;大学生择业创业的导师;大学校园文化的引导者和创造者以及大学生心理健康的培育者。     

Testing How Management Matters in an Era of Government by Performance Management

Public administration finds itself in an era of government byperformance management, which is reflected in the widespreadassumption that management is a key determinant of performance,and that it is reasonable to expect managers to measurably improveorganizational effectiveness. This article joins a growing literaturein seeking to conceptualize and empirically test how externalenvironmental influences and internal management factors combineto create performance, relying on data from the 2002–2003National Administrative Studies Project (NASP-II) survey ofstate government health and human services officials. We categorizemanagerial efforts to facilitate organizational performanceas determined either through their interactions with the organizationalenvironment, or through employing workable levers to changeinternal organizational culture, structure, and technology.Among the external environmental variables we find that thesupport of elected officials and the influence of the publicand media have a positive impact on effectiveness. Among internalmanagement choices, the ability to create a developmental organizationalculture, establish a focus on results through goal clarity,and decentralize decision-making authority are all positivelyassociated with organizational effectiveness.     

Four‐Month Attentional Regulation and Its Prediction of Three‐Year Compliance

The extent to which 4‐month attentional regulation during an infant‐mother still‐face situation and 12‐month attachment security during the Strange Situation predicted rates of compliance and noncompliance during a cleanup task at 36 months was examined longitudinally in 70 infant‐mother dyads. Hierarchical multiple regression analyses indicated that infants who showed more attentional regulation during the still‐face situation with mothers later showed a higher rate of committed compliance and a lower rate of situational compliance during cleanup with mothers. Furthermore, lower levels of attentional regulation were later associated with higher rates of assertive behavior, but only for infants in an insecure attachment relationship.     

Les normes comptables comme institution du capitalisme. Une analyse du passage aux normes IFRS en Europe à partir de 2005

In 2002, the European Union changed direction from its past efforts to harmonise European accounting systems and turned instead to a subcontractor, requiring listed European companies to adopt accounting standards drawn up by a private London-based organisation, the IASB (International Accounting Standards Board), which had until then had no public mandate. This decision was all the more significant because the EU has no institutional means of control over the IASB, although it was the first geographical group to decide to impose these standards on its businesses. IASB standards are rooted in the “anglo-saxon” tradition of standardisation for the financial markets, and the view of the enterprise they promote diverges from the accounting traditions of continental European capitalism. This article shows how the new standards are transforming the concept of enterprise, and examines the span of factors that brought about what is a major institutional change for European capitalism. — Special issue: New patterns of institutions.     

The organizational formation of atmospheric science

In this paper, the history of atmospheric science is traced, from the earliest discussions from meteorologists, and the first meetings to describe this new research area. The development of atmospheric science proceeded in three phases with each phase being marked by the attempt to organize resources, and bring into the atmospheric science network, scientists from other fields. I argue here that this construction of a science provides evidence of the social nature of science as well as the importance of organizations to the creation of a science.     

Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms

Summary.  In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.