22 citations · 38 across the 12 of their papers we have counts for
7 papers · 1 filter
Feasible pairwise pseudo-likelihood inference on spatial regressions in irregular lattice grids: the KD-T PL algorithm
Giuseppe Arbia, Niccolo Salvini
Spatial regression models are central to the field of spatial statistics. Nevertheless, their estimation in case of large and irregular gridded spatial datasets presents considerab…
Spatial sampling design to improve the efficiency of the estimation of the critical parameters of the SARS-CoV-2 epidemic
Giorgio Alleva, Giuseppe Arbia, Piero Demetrio Falorsi +2
The pandemic linked to COVID-19 infection represents an unprecedented clinical and healthcare challenge for many medical researchers attempting to prevent its worldwide spread. Thi…
On Spatial Lag Models estimated using crowdsourcing, web-scraping or other unconventionally collected data
Giuseppe Arbia, Vincenzo Nardelli
The Big Data revolution is challenging the state-of-the-art statistical and econometric techniques not only for the computational burden connected with the high volume and speed wh…
Post-sampling crowdsourced data to allow reliable statistical inference: the case of food price indices in Nigeria
Giuseppe Arbia, Gloria Solano-Hermosilla, Fabio Micale +2
Sound policy and decision making in developing countries is often limited by the lack of timely and reliable data. Crowdsourced data may provide a valuable alternative for data col…
Reduced-bias estimation of spatial econometric models with incompletely geocoded data
Giuseppe Arbia, Maria Michela Dickson, Giuseppe Espa +2
The application of state-of-the-art spatial econometric models requires that the information about the spatial coordinates of statistical units is completely accurate, which is usu…
Measurement error induced by locational uncertainty when estimating discrete choice models with a distance as a regressor
Giuseppe Arbia, Paolo Berta, Carrie B. Dolan
Spatial microeconometric studies typically suffer from various forms of inaccuracies that are not present when dealing with the classical regional spatial econometrics models. Amon…
A bivariate marginal likelihood specification of spatial econometric modeling of very large datasets
Giuseppe Arbia
This paper proposes a bivariate marginal likelihood specification of spatial econometrics models that simplifies the derivation of the log-likelihood and leads to a closed form exp…