arXiv subjects

Conor Kresin

Publications and source records attributed to Conor Kresin.

6 recordsLinked to original sources

arXiv2026

Optimal Estimating Equations for Compact-Memory Hawkes Processes

Likelihood is standard for Hawkes-process inference, while less computationally demanding methods have largely developed separately. We show that least squares, Tak\'acs--Fiksel, and related moment-based estimators form a single class of compensator-based estimating equations, with the likelihood score as the efficient benchmark. For fixed-dimensional multivariate Hawkes processes with compact memory, nonlinear positive links, and signed kernels allowing inhibition, every suitably regular predictable functional of a fixed lag window yields an unbiased estimating equation when integrated against $\text{d} N-\lambda\,\text{d} t$. Under common regularity, identification, and rank conditions, estimators based on every admissible finite library achieve uniform high-probability and pointwise almost-sure $\mathcal O(\sqrt{\log(T)/T})$ rates, asymptotic normality with Godambe covariance, and admit feasible two-step optimal weighting. A projection identity quantifies each library's exact efficiency loss as the score information outside its predictable span; a two-point bound shows the root-$T$ scale cannot be improved uniformly. Although compact memory localizes the intensity rather than the stationary law, exponential forgetting yields Bernstein-type concentration and transfers the theory to nonstationary starts after a logarithmic burn-in. Within this scope, the compensator class is exhaustive for finite-library comparisons: it contains the score, gives admissible libraries common guarantees, and quantifies their efficiency gaps exactly.

math.ST↗

arXiv2026

Orientation in Poisson Cluster Processes via Imaginary Bispectra

We study what remains detectable about one-sided Poisson cluster processes after cluster orientation is erased. We construct matched reversible cluster nulls preserving intensity and the full Bartlett spectrum, showing that second-order structure alone need not identify temporal direction. For stationary Poisson branching clusters, we derive the Fourier--Stieltjes transform of the reduced third cumulant and show that, in the $L^1$ third-cumulant regime, a nonzero imaginary factorial bispectrum certifies orientation. We also give explicit orientation-erased nulls, reversible spectral matches for monotone Hawkes kernels, and finite-window third-order orientation contrasts.

math.PR↗

arXiv2026

Causal inference for spatiotemporal point processes in the presence of outcome spillover and carryover

We develop a framework for causal inference with continuous spatiotemporal point-process outcomes under cell-level interventions and outcome spillover. Potential outcomes are indexed by full treatment allocations, and the observed post-treatment process is represented as an unlabelled superposition of latent control and treatment components. On the observed design support, expected post-treatment event counts in any spacetime region under a given treatment allocation are identified under consistency, exchangeability, and positivity; off-support contrasts are identified relative to a regime-stable structural point-process model. Estimation is likelihood-based and implemented with stochastic EM. To understand when this is feasible, we analyse a predictable blockwise hard-EM surrogate and show nonasymptotic contraction of estimation error to a statistical floor governed by locally ambiguous regions. This yields plug-in guarantees for cell-level and global causal functionals, and clarifies the additional array conditions needed for unnormalised growing-window contrasts. The framework covers history dependent spatiotemporal point processes including Poisson and Hawkes models, with applications to settings such as epidemiology, seismology, and finance. We provide an application assessing the causal effect of injecting wastewater into the ground on seismic activity in Oklahoma.

stat.ME↗

arXiv2026

Spectral analysis of multivariate stationary Hawkes processes

We establish the asymptotic validity of frequency-domain inference for stationary multivariate Hawkes processes under mild conditions, bridging the gap between theory and application. By developing upper-bounds on the reduced cumulant measures from the cluster representation of the Hawkes processes, we prove a functional central limit theorem and, as a consequence, consistency of the Whittle estimator under stationarity alone (i.e., the spectral radius of the interactions matrix $\rho(\boldsymbol\nu)<1$), applicable to Hawkes processes with heavy-tailed mutual-excitation kernels. Under mild extra moment conditions, we further obtain asymptotic normality with an explicit limiting covariance in terms of second- and fourth-order cumulant spectral densities. We also propose a simple frequency-domain method to detect joint independence of subprocesses of a multivariate Hawkes process. The performance of the Whittle estimator and the test of independence are demonstrated via simulation studies.

math.ST↗

arXiv2025

Change Point Detection and Mean-Field Dynamics of Variable Productivity Hawkes Processes

Many self-exciting systems change because endogenous amplification, as opposed to exogenous forcing, varies. We study a Hawkes process with fixed background rate and kernel, but piecewise time-varying productivity. For exponential kernels we derive closed-form mean-field relaxation after a change and a deterministic surrogate for post-change Fisher information, revealing a boundary layer in which change time information localises and saturates, while post-change level information grows linearly beyond a short transient. These results motivate a Bayesian change point procedure that stabilizes inference on finite windows. We illustrate the method on invasive pneumococcal disease incidence in The Gambia, identifying a decline in productivity aligned with pneumococcal conjugate vaccine rollout.

stat.OT↗

arXiv2024

Multivariate Representations of Univariate Marked Hawkes Processes

Univariate marked Hawkes processes are used to model a range of real-world phenomena including earthquake aftershock sequences, contagious disease spread, content diffusion on social media platforms, and order book dynamics. This paper illustrates a fundamental connection between univariate marked Hawkes processes and multivariate Hawkes processes. Exploiting this connection renders a framework that can be built upon for expressive and flexible inference on diverse data. Specifically, multivariate unmarked Hawkes representations are introduced as a tool to parameterize univariate marked Hawkes processes. We show that such multivariate representations can asymptotically approximate a large class of univariate marked Hawkes processes, are stationary given the approximated process is stationary, and that resultant conditional intensity parameters are identifiable. A simulation study demonstrates the efficacy of this approach, and provides heuristic bounds for error induced by the relatively larger parameter space of multivariate Hawkes processes.

stat.ME↗