A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990–2015

Population and Environment - Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is...     

Data tilting for time series

Summary. We develop a general methodology for tilting time series data. Attention is focused on a large class of regression problems, where errors are expressed through autoregressive processes. The class has a range of important applications and in the context of our work may be used to illustrate the application of tilting methods to interval estimation in regression, robust statistical inference and estimation subject to constraints. The method can be viewed as 'empirical likelihood with nuisance parameters'.     

Interactionism,Social Organization,and Social Processes: Looking Back and Moving Ahead

With its roots in American pragmatism, symbolic interactionism has created a distinctive perspective and produced numerous important contributions and now offers significant prospects for the future. In this article, I review my intellectual journey with this perspective over forty years. This journey was initiated within the American society, sociology, and symbolic interaction of circa 1960. I note many of the contributions made by interactionists since that time, with particular focus on those who have contributed to the study of social organization and social process. I offer an agenda for the future based on currently underdeveloped areas that have potential. These are inequality orders, institutional analysis, collective action across space and time, and the integration of temporal and spatial orders. The article concludes with calls for further efforts at cross‐perspective dialogues, more attention to feminist scholars, and an elaborated critical pragmatism.     

On the shrinkage of local linear curve estimators

Local linear curve estimators are typically constructed using a compactly supported kernel, which minimizes edge effects and (in the case of the Epanechnikov kernel) optimizes asymptotic performance in a mean square sense. The use of compactly supported kernels can produce numerical problems, however. A common remedy is ridging, which may be viewed as shrinkage of the local linear estimator towards the origin. In this paper we propose a general form of shrinkage, and suggest that, in practice, shrinkage be towards a proper curve estimator. For the latter we propose a local linear estimator based on an infinitely supported kernel. This approach is resistant against selection of too large a shrinkage parameter, which can impair performance when shrinkage is towards the origin. It also removes problems of numerical instability resulting from using a compactly supported kernel, and enjoys very good mean squared error properties.     

“Facing” leaders: Facial expression and leadership perception

This experimental study investigated the effect of a leader's expression of happy versus nervous emotions on subsequent perceptions of leadership and ratings of traits associated with implicit leadership theories (ILTs). Being fast and universally understood, emotions are ideal stimuli for investigating the dynamic effects of ILTs, which were understood in this study in terms of the constraints that expressed emotions impose on the connectionist networks that activate ILTs. The experimental design contrasted videotaped and still frame presentations of a leadership event; however, this methodological factor had no significant effects and analyses were thus collapsed across this factor. Key findings were that the expression of a happy versus nervous emotion at the end of a problem-solving sequence had multiple effects: happy emotions resulted in higher leadership ratings, higher trait ratings, greater correlations among trait ratings, and greater dependence of trait ratings on leadership perceptions. An exploratory model suggested that leadership impressions mediated the effects of facial emotions on trait ratings. The discussion further links the study findings with interpretations in terms of ILTs and many types of constraints on these cognitive structures. It also suggests ways to integrate these ideas with advances in neuroscience research.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Observed and parent‐reported conscience in childhood: Relations with bullying involvement in early primary school

This exploratory study aimed to examine which components of early childhood conscience predicted bullying involvement around school entry. In the population‐based Generation R Study, teacher reports of bullying involvement and parent reports of conscience were available for 3,244 children (M age = 6.7 years). Higher levels of overall conscience predicted lower bullying perpetration scores, independently of intelligence quotient, temperamental traits and sociodemographic characteristics. Particularly, the subscales guilt, confession, and internalized conduct, and to a lesser extent empathy, predicted bullying perpetration. Conscience was not related to victimization. Similar results were found using observations during so‐called ‘cheating games’ (subsample N = 450 children). Findings suggest that improving children's understanding of moral standards and norms may be a potential target for bullying intervention programs in early primary school.