arXiv subjects

Aurélien Nicosia

Publications and source records attributed to Aurélien Nicosia.

5 recordsLinked to original sources

arXiv2026

Identifiability and likelihood estimation for homogeneous angular regression with random coefficients

Repeated directional responses may vary between individuals in both overall orientation and sensitivity to explanatory cues. We study homogeneous angular regression with correlated Gaussian cue coefficients and a circular random intercept, separating observation concentration from resultant length. Constructive identification conditions recover the joint coefficient law, intercept concentration and observation-error parameters, including unknown uniform contamination. The argument combines repeated contrasts, an arc-support restriction and joint Fourier deconvolution, and extends to exogenous continuous designs. Integration in coefficient coordinates yields smooth population scores even when resultant cancellation prevents a regular latent mode. A fixed-proposal adaptive importance algorithm evaluates the marginal likelihood and its derivatives. In 8,000 independent simulated datasets, 7,998 fits pass numerical checks, and 12,799 of 12,800 local profile intervals are available. Correlation profile-set coverage nevertheless ranges from 91.9% to 94.8% at the nominal 95% level. Greater precision and numerical availability therefore do not ensure calibrated inference. An exploratory extension to 150 Canadian weather stations is evaluated on 259,807 three-hour forecasts from a new 2026 period after fitting on 2023-2025 data. With circular station rotations in all matched models, independent station slopes give modest predictive gains over slopes shared within regions, in all five regions. Estimating their correlations adds no useful predictive gain; this does not assess parameter uncertainty, and predictive calibration remains imperfect. The application uses a noncancelling mean and Laplace integration, so it illustrates the model class rather than testing the proposed algorithm.

stat.ME↗

arXiv2026

A Leave-One-Out Influence Statistic for Density-Based Outlier Detection

We propose a density-based leave-one-out influence score for unsupervised outlier detection. The motivation is that outliers are naturally associated with regions of very small probability density, but direct leave-one-out density refitting can be computationally prohibitive. We use the Linear-Blend Frequency Polygon (LBFP) estimator and define a score that compares the full-sample fitted density at an observation with the fitted density obtained after removing that observation, while keeping the grid and bandwidth fixed. The resulting statistic measures a relative density perturbation at the observation's own location. For the LBFP estimator, this score has an exact closed-form update, so the density estimator does not need to be refitted for each observation. This preserves a direct density interpretation while making the method computationally efficient for large samples. We study the score under contamination and show that regular positive-density observations and contamination-driven observations have distinct asymptotic orders. Simulations over a broad range of contamination models illustrate these theoretical regimes, show competitive performance relative to standard benchmarks, and document computing time. A credit-card fraud application with 29 variables illustrates that the method works well on a large real data set.

stat.ME↗

arXiv2026

Tropical Viterbi Tubes for Decoding Uncertainty in Hidden Markov Models

Hidden Markov models are widely used to infer latent state sequences from sequential data, but Viterbi decoding reports only one most likely complete path. When decoded states carry scientific meaning, this single maximizer can conceal pathwise uncertainty created by multiple near-optimal trajectories. Conditional on a fitted HMM, we introduce the tropical Viterbi tube: the set of hidden trajectories whose complete-data log-score lies within a tolerance of the Viterbi optimum. State, transition, and change-status projections show which local features remain compatible with globally near-optimal complete paths, giving a pathwise uncertainty layer for HMMs in sequence analysis, ecology, finance, biomedical monitoring, and related domains. The tube is a posterior superlevel set on complete hidden-path space, with tolerance interpreted as a log posterior-odds loss relative to a Viterbi path. Calibrating the tolerance to a target posterior mass gives an HPD-threshold credible region for the complete latent path and conservative simultaneous projected bands. We prove monotonicity, step-function behavior, and deterministic stability guarantees, and compute projected tubes exactly by max-plus forward-backward recursions in O(TK^2) time for dense transitions. Posterior tube mass and HPD calibration are separate pathwise calculations approximated by FFBS. In a public bat-tracking application, robust foraging tube segments are enriched for feeding buzzes, whereas robust commuting segments are depleted: at eta = 0.005, enrichment is 2.25 with 95% bootstrap interval (1.73, 2.85) for robust foraging and 0.27 with interval (0.16, 0.44) for robust commuting.

stat.ME↗

arXiv2016

A Multi-State Conditional Logistic Regression Model for the Analysis of Animal Movement

A multi-state version of an animal movement analysis method based on conditional logistic regression, called Step Selection Function (SSF), is proposed. In ecology SSF is developed from a comparison between the observed location of an animal and randomly sampled locations at each time step. Interpretation of the parameters in the multi-state model and the impact of different sampling schemes for the random locations are discussed. We prove the equivalence between the new model and a random walk model on the plane. This equivalence allows one to use both pure movement and local discrete choice behaviors in identifying the model's hidden states. The new method is used to model the movement behavior of GPS-collared bison in Prince Albert National Park, Canada. The multi-state SSF successfully teases apart areas used to forage and to travel. The analysis thus provides valuable insights into how bison adjust their movement to habitat features, thereby revealing spatial determinants of functional connectivity in heterogeneous landscapes.

stat.ME↗

arXiv2016

A General Hidden State Random Walk Model for Animal Movement

In this paper, we propose a general hidden state random walk model to describe the movement of an animal that takes into account movement taxis with respect to features of the environment. A circular-linear process models the direction and distance between two consecutive localizations of the animal. A hidden process structure accounts for the animal's change in movement behavior. The originality of the proposed approach is that several environmental targets can be included in the directional model. An EM algorithm is devised to fit this model and an application to the analysis of the movement of caribou in Canada's boreal forest is presented

stat.ME↗