arXiv subjects

Theofanis Papamichalis

Publications and source records attributed to Theofanis Papamichalis.

4 recordsLinked to original sources

arXiv2026

State-Space Modeling of Time-Varying Spillovers on Networks

Crime counts in city neighbourhoods, disease counts in counties, and sales at firms joined by trade are naturally represented as counts on the nodes of a network. In each case a high count at one node can raise the counts at the nodes linked to it next period. The strength of that spillover changes over time, and standard network autoregressions hold it fixed. We therefore use a network state-space model, in which the spillover is a coefficient that drifts and a filter estimates its value in each period. What the data reveal about that coefficient depends on the network. It is learned by contrasting nodes whose neighbours have high counts with nodes whose neighbours have low ones. If every node is linked to every other, all nodes share the same neighbours, the contrasts vanish, and the spillover is not identified. Robustness is often checked by refitting with the links spread evenly, and a stable coefficient is read as reassurance. That refit is the same model with the spillover rescaled, so it cannot disagree. Forecasts carry a second warning: beyond two steps ahead, simulation averages a quantity with no finite mean, and the output gives no sign of it. We give a measure of what a network and a data set reveal about the spillover, the accuracy the filter can reach, and an exact test of whether the network matters. Burglaries in Chicago, COVID-19 cases in Texas counties, and measles cases in the Weser--Ems districts illustrate all three. On both disease datasets the model outperforms every competing forecast in the comparison.

stat.ME↗

arXiv2026

Replicable Conformal Prediction

Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration threshold inherits the randomness of the data. Wherever deployments must be audited, cached, or approved across sites, this instability is costly: no one can verify that two calibrations produced the same object. We ask two questions: when can independent calibrations yield the identical classifier, and what must that agreement cost? Perfect agreement is impossible, since a procedure that almost always returns one fixed answer cannot remain valid for every distribution, and exact agreement through shared randomness forces the procedure to ignore its data. Sharing a single random seed and rounding the calibrated threshold up to a coarse shared grid resolves the tension: the deployed classifier becomes identical across analysts with any desired probability, coverage guarantees survive, and the price is a quantified increase in set size and calibration data. Matching lower bounds show that no threshold method can pay less, and the method's one tuning constant vanishes asymptotically. Without any shared seed, a fixed grid still confines all analysts to two adjacent classifiers, and no method does better. Replicability also blocks gaming: selecting the most favorable of many recalibrations barely moves a replicable classifier, while the same selection silently undercovers standard conformal prediction. Experiments on real ImageNet outputs, a four-hospital site split, and four language-model families match the theory, including the measured sample-cost frontier.

stat.ML↗

arXiv2026

Separating Time-Varying Network Composition from Predictive Dependence under Noisy Network Measurement

A common question about networked time series is whether outcomes changed because shocks transmit more strongly or because the pattern of connections changed. Standard practice inserts a recorded network into an outcome regression and reads movements of the fitted coefficient as changes in transmission strength. When the network is latent, time varying, and measured with error, this reading fails: changes in strength and changes in composition can produce the same outcome distribution at a single date, and the population coefficient moves under composition changes alone. The question becomes answerable when outcomes are analyzed jointly with repeated noisy measurements of the network, such as paired reports of bilateral trade flows. For the joint model we establish necessary and sufficient conditions for local identification, estimators of the strength and composition paths, a simultaneous confidence band for the strength path, confidence sets that remain exact under weak identification, breakdown bounds under common reporting bias, and an exactly sized test that detects changes on the observed path and attributes them to strength or to composition. Simulations assess each procedure at its stated boundary. On a mirror-reported trade panel of eighteen economies over 1995 to 2020, the diagnostics flag exactly the crisis years and the composition coordinate attached to European Union membership declines by roughly two thirds. The estimand is predictive dependence, not a causal effect.

stat.ME↗

arXiv2026

Bayesian Predictive Synthesis for Dynamic Networks: Forecasting and Identifying Structural Mechanisms

Networks are shaped by competing structural mechanisms, such as communities, geometry, or hubs. In a dynamic network the most predictive mechanism can change, and a model tied to one mechanism, or to fixed weights, cannot adapt as the dominant structure shifts. We develop dynamic Bayesian predictive synthesis for networks, in which a mechanism is an agent forecasting the next snapshot's edges and a synthesis layer combines them with time-varying weights. At each step the method returns a calibrated edge forecast and inference on the mechanism weights, with intervals valid given the fitted agents, so it also reports which mechanism is most informative. Inference of this kind requires a sparse-safe parametrization and an identification theory, under which a single graph identifies and estimates the weights. A sharp threshold separates distinguishable from indistinguishable mechanisms, a change in the active mechanism is tracked at an optimal per-switch cost, and for a single snapshot the method reduces to calibrated link prediction. On real networks, simulations, and benchmarks, the synthesis gives accurate, calibrated forecasts and recovers the leading mechanism when

cs.SI↗