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The modern world has seen a rapid evolution of the technology of biometric authentication, prompted by an increaing urgency to ensure a system's security. The need for efficient authentication systems has skyrocketed since 9/11, and the proposed inclusion of digitized photos in passports shows the importance of biometrics in homeland security today. Based on a person's essentially unique biological traits, these methods are potentially more reliable than traditional identifiers like PINs and ID cards. This paper focuses on demonstrating the use of statistical models in devising efficient authentication systems today that are capable of handling real-life applications. First, we propose a novel Gaussian Mixture Model-based face authentication approach in the frequency domain by exploiting the well-known significance of phase in face identification and illustrate that our method is superior to the non-model based state-of-the-art system called the Minimum Average Correlation Energy (MACE) filter in terms of performance on a database of 65 people under extreme illumination conditions. We then introduce a general statistical framework for assessing the predictive performance of a biometric system (including watch-list detection) and show that our model-based system outperforms the MACE system in this regard as well. Finally, we demonstrate how this framework can be used to study the watch-list performance of a biometric system.

The p_1 model is a directed random graph model used to describe dyadic interactions in a social network in terms of effects due to differential attraction (popularity) and expansiveness, as well as an additional effect due to reciprocation. In this article we carry out an algebraic statistics analysis of this model. We show that the p_1 model is a toric model specified by a multi-homogeneous ideal. We conduct an extensive study of the Markov bases for p_1 models that incorporate explicitly the constraint arising from multi-homogeneity. Our results are directly relevant to the estimation and conditional goodness-of-fit testing problems in p_1 models., 22 pages. 4 figures depicting relevant Markov moves. One section removed from previous version.

We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record-systems need to be integrated for posterior analysis. Our method generalizes the Fellegi-Sunter theory for linking records from two datafiles and its modern implementations. The multiple record linkage goal is to classify the record K-tuples coming from K datafiles according to the different matching patterns. Our method incorporates the transitivity of agreement in the computation of the data used to model matching probabilities. We use a mixture model to fit matching probabilities via maximum likelihood using the EM algorithm. We present a method to decide the record K-tuples membership to the subsets of matching patterns and we prove its optimality. We apply our method to the integration of three Colombian homicide record systems and we perform a simulation study in order to explore the performance of the method under measurement error and different scenarios. The proposed method works well and opens some directions for future research.

We describe new results for upper and lower bounds on the entries in multi-way tables of counts based on a set of released and possibly overlapping marginal tables which have practical import for assessing disclosure risk. In particular, we present a generalized version of the shuttle algorithm proposed by Buzzigoli and Giusti that is proven to compute sharp integer bounds for an arbitrary set of fixed marginals.

Online tools, including social networks, are becoming more prevalent in teacher support and professional development. However, little is known about how existing online communities of educators function. If online social networks are to be used as part of ongoing teacher development, training or support, we must better understand successful networks and their participants. We present an analysis of Classroom 2.0, an established, active and growing community dedicated to helping teachers incorporate web 2.0 technologies into their classrooms. Web 2.0 technologies are collaborative tools, such as wikis, blogs, and podcasts that allow users to share content and/or create it together. We drew from the Communities of Practice literature to explore the structure of ties and the content of interactions between members. Latent Dirichlet Allocation was used to analyze the content of network interactions, and ego networks were used to characterize patterns of interaction. We found two separate sub-communities within Classroom 2.0, one dedicated toward substantive discussions of education and technology, the other dedicated toward forming relationships with new colleagues.