OOIR: Observatory of International Research

Papers

(The H4-Index of Spatial Statistics is 20. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-05-01 to 2026-05-01.)

Article	Citations
A flexible class of priors for orthonormal matrices with basis function-specific structure	85
A more accurate estimation with kernel machine for nonparametric spatial lag models	82
Explicit modeling of density dependence in spatial capture-recapture models	64
Determination of the best weight matrix for the Generalized Space Time Autoregressive (GSTAR) model in the Covid-19 case on Java Island, Indonesia	45
Bayesian geographically weighted regression using Fused Lasso prior	45
Spatial linear discriminant analysis approaches for remote-sensing classification	38
Computationally efficient localised spatial smoothing of disease rates using anisotropic basis functions and penalised regression fitting	33
Capturing spatial dependence of COVID-19 case counts with cellphone mobility data	29
Derivative-based spatial mediation with INLA-SPDE	27
Optimal prediction of positive-valued spatial processes: Asymmetric power-divergence loss	26
Tests for isotropy in spatial point patterns – A comparison of statistical indices	26
Dynamic spatial regimes for spatial panel data	25
Voronoi linkage between mismatching voting stations and census tracts in analyzing the 2018 Brazilian presidential election data	24
Estimating the growth rate of infection during the early phase of a pandemic like COVID-19	24
Transfer learning for high dimensional spatial autoregressive model	23
Bayesian modeling and clustering for spatio-temporal areal data: An application to Italian unemployment	22
Measuring unit relevance and stability in hierarchical spatio-temporal clustering	22
Correlation-based hierarchical clustering of time series with spatial constraints	22
A simultaneous system of dynamic spatial stochastic frontier models with dependent error components and inefficiency determinants	21
Using neural networks to estimate parameters in spatial point process models	20