Planning Family Spending

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Geostatistical modeling in the presence of interaction between the measuring instruments, with an application to the estimation of spatial market potentials

This paper addresses the problem of recovering the spatial market potential of a retail product from spatially distributed sales data. In order to tackle the problem in a general way, the concept of spatial potential is introduced. The potential is concurrently measured at different spatial locations and the measurements are analyzed in order to recover the spatial potential. The measuring instruments used to collect the data interact with each other, that is, the measurement at a given spatial location is affected by the concurrent measurements at other locations. An approach based on a novel geostatistical model is developed. In particular, the model is able to handle both the measuring instrument interaction and the missing data. A model estimation procedure based on the expectation-maximization algorithm is provided as well as standard inferential tools. The model is applied to the estimation of the spatial market potential of a newspaper for the city of Bergamo, Italy. The estimated spatial market potential is eventually analyzed in order to identify the areas with the highest potential, to identify the areas where it is profitable to open additional newsstands and to evaluate the newspaper total market volume of the city.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS588 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Influences of Evidence, Beliefs, and Emotion

Within the reasoning literature, most investigations of motivated reasoning, belief-biased reasoning and the effects of emotional material have all been conducted separately from each other. Motivated reasoning theories state that reasoning can be goal-directed, and all future processing is allocated towards achieving an end goal or justifying a position. Dual process theories of reasoning, on the other hand, allow for analytic thinking to discriminate between strong and weak arguments. Additionally, theories of emotion in reasoning state that emotional content can negatively impact future processing. Our goal was to investigate the interaction of argument strength, prior belief and emotional content in argument evaluation over the course of three experiments (N = 360). Participants completed questionnaires that involved reading conversation transcripts and ranking the strength of the evidence presented in the conversation. Conversations were varied on their argument strength, believability, and emotional content. Following the conversations, we asked participants to personally rank the believability and emotionality of the topics used within the experiment. We found that most participants were sensitive to the strength of the evidence presented in the conversations, but a small minority were more likely to appraise the evidence based previous beliefs. The impact of emotional versus neutral content was found to minimally impact the appraisal of presented evidence. These data suggest an explanation based on both motivated reasoning theories and dual process theories of reasoning. Most individuals were able to discriminate between strong and weak evidence, as predicted by dual process theories. However, some individuals were more sensitive to the believability of the presented statements and exhibited examples of belief bias phenomena. As motivated reasoning theories would predict, their appraisal of evidence may have been guided towards an end-goal that was congruent with their previous beliefs. Individual differences played a large role in our current findings, and future directions should investigate the driving forces behind these differences

Clean Clothes Newsletter No. 23

Newsletter of Clean Clothes Campaign detailing efforts of various European campaigns and issues surrounding fair labor standards

Impact of rankings

This chapter discusses the influence league table performance can have on an institution, affecting its student recruitment, its funding and even its leadership. It goes on to discuss the impact on the sector as a whole in encouraging a frantic reputation race and leading institutions to concentrate their efforts and resources on a single area of activity, research, with detrimental effects on individual institutions and the sector. The chapter also looks at the potential positive impact that a well-designed ranking system could deliver and outlines the basic principles and ‘lessons learned’ that would shape the design of such a system

Learning Higher-order Transition Models in Medium-scale Camera Networks

We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks. 1