5 citations · 7 across the 8 of their papers we have counts for
8 papers
Sampled Grid Pairwise Likelihood (SG-PL): An Efficient Approach for Spatial Regression on Large Data
Giuseppe Arbia, Vincenzo Nardelli, Niccolo Salvini
Estimating spatial regression models on large, irregularly structured datasets poses significant computational hurdles. While Pairwise Likelihood (PL) methods offer a pathway to si…
Evaluating Large Language Model Capabilities in Assessing Spatial Econometrics Research
Giuseppe Arbia, Luca Morandini, Vincenzo Nardelli
This paper investigates Large Language Models (LLMs) ability to assess the economic soundness and theoretical consistency of empirical findings in spatial econometrics. We created…
On Spatio-Temporal Stochastic Frontier Models
Elisa Fusco, Giuseppe Arbia, Francesco Vidoli +1
In the literature on stochastic frontier models until the early 2000s, the joint consideration of spatial and temporal dimensions was often inadequately addressed, if not completel…
Detecting Spatial Outliers: the Role of the Local Influence Function
Giuseppe Arbia, Vincenzo Nardelli
In the analysis of large spatial datasets, identifying and treating spatial outliers is essential for accurately interpreting geographical phenomena. While spatial correlation meas…
On Robust Measures of Spatial Correlation
Vincenzo Nardelli, Giuseppe Arbia
As a rule statistical measures are often vulnerable to the presence of outliers and spatial correlation coefficients, critical in the assessment of spatial data, remain susceptible…
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…