2026-2027

Title : The K-function for point patterns on surfaces

Abtract : The K-function is a well-known summary statistic used to determine whether a point pattern exhibits aggregation, repulsion, or complete spatial randomness. Its development has been applied to different surfaces by leveraging the invariance properties of the surface on which the point pattern is defined, such as the unit sphere or the unit sphere cross time. In this work, we present two extensions of the K-function to more general surfaces. The first extension is based on tangent plane approximations of the surface, while the second relies on geodesic distance. We provide computational algorithms and conduct simulation studies to demonstrate the performance of the extended K-function in detecting various point pattern properties.

Coauthors : Rasmus Waagepetersen (Aalborg University), Edward Cohen (Imperial College London), and Scott Ward (Imperial College London)

Title : Selective inference and its applications

Abtract : Classical statistics centers around the setting where the questions that we ask about our data are specified in advance, before we even look at our data. However, in contemporary settings, often the data analyst comes up with a question only after looking at the data. Classical methods break down in this setting: for instance, standard hypothesis tests do not achieve type 1 error control, and confidence intervals do not attain the nominal coverage. In this brief talk, I will introduce the field of selective inference, which encompasses a number of recent approaches to conduct inference on hypotheses or parameters that are selected from the data, rather than specified in advance.

Title : Minority representation and fairness in network ranking, with an application to school contact diary data

Abstract : Considerations of bias, fairness and representation are a prerequisite of responsible modern statistics, including statistical network analysis. Systematic bias can arise in network analysis when observation errors depend on node attributes, potentially distorting node rankings and group representation. In this talk, we are motivated by a high school contact network constructed from self-reported contact diaries, where nodes represent students, directed edges represent reported contacts, and groups are defined by sex. For networks with two known groups of nodes, we define minority representation as the proportion of nodes from the smaller group among the top-ranked nodes. We characterize its asymptotic behavior in degree-based rankings under a two-group stochastic block model. We model observation error through group-dependent missing-edge mechanisms and develop statistical methods to estimate the error rates and test for systematic bias. When correction is necessary, we apply a ranking correction procedure that targets the estimated group representation expected in the absence of observation error. Application to the contact diary data provides evidence that male students are more likely to omit contacts with female students than vice versa. These findings highlight the importance of accounting for group-dependent observation errors when assessing and correcting group representation in network rankings.  Joint work with Hui Shen and Peter MacDonald.

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