01
Time Series and Spatial Statistics
Statistical modeling and inference for dependent data, including time series, conditional spatial regression, two-dimensional ARMA models, random fields, geostatistics, and spatio-temporal processes.
Research
Research and interests
Research interests and current projects in the Department of Statistics at UFPE.
Areas of interest
All areas
Current research and other methodological areas I plan to explore.
Reading guide
02
Statistical inference and asymptotic theory
Classical and Bayesian inference, statistical information, divergence-based estimation and hypothesis testing, and asymptotic theory.
03
Statistical Image Processing
Statistical modeling and inference for images, from SAR/PolSAR and environmental remote sensing to emerging biomedical-imaging applications.
04
Regression Models and Estimating Equations
Statistical regression for non-Gaussian and correlated outcomes: marginal GEE, mixed and Bayesian hierarchical models, and flexible distributional regression.
05
Information geometry
Statistical manifolds, geodesic estimation and hypothesis testing, and geometric computation for structured probabilistic models.
06
Causal inference
Causal identification, semiparametric effect estimation, and spatial or longitudinal causal inference under explicit research-design assumptions.
Research library
Explore the reading guides A starting point for each research area
Annotated references to help students build foundations before choosing a research question.
Projects and investigations
Work in progress
Research questions that guide work in progress.
Work in progress2026
Inference for spatially dependent data
Methodological research on spatial dependence, model adequacy and inferential properties.
Work in progress2026
Inference for polarimetric indices
Probability modeling and uncertainty quantification for summaries derived from polarimetric radar imagery.
Work in progress2026
Statistical diagnostics for spatial models
Study of methods for assessing model adequacy in spatially dependent observations.
Work in progress2026
Uncertainty in spatially correlated SAR imagery
Investigation of how spatial dependence affects the interpretation of statistics derived from radar imagery.
Collaboration interests
Possible directions for scientific collaboration
Broad interests for initial conversations. Specific proposals, data and unpublished methods are discussed individually; no vacancies or formal partnerships are announced.
Applied studies1
Remote sensing in environmental and health studies
Interest in collaborations combining remote-sensing data, spatial analysis, and domain expertise on environmental or population-level questions.
Feasibility question: The question, data, and methodological feasibility would be defined with collaborators.
Methodological research1
Modeling and inference for spatial and spatio-temporal data
Interest in methodological questions involving dependence, coherent model construction, and uncertainty quantification.
Feasibility question: Any project depends on a well-defined scientific formulation and verifiable assumptions.
Scientific software1
Scientific computing for spatial inference
Interest in reproducible software and critical comparisons of computational methods for spatial statistical models.
Feasibility question: Methods and implementation details would depend on the model and the team.