| Quantile regression: theory and applications C Davino, M Furno, D Vistocco John Wiley & Sons, 2013 | 497 | 2013 |
| Quantile regression: theory and applications C Davino, M Furno, D Vistocco John Wiley & Sons, 2013 | 497 | 2013 |
| Quantile regression: estimation and simulation, Volume 2 M Furno, D Vistocco John Wiley & Sons, 2018 | 75 | 2018 |
| Do consumers’ values and attitudes affect food retailer choice? Evidence from a national survey on farmers’ market in Germany G Cicia, M Furno, T Del Giudice Agricultural and Food Economics 9, 1-21, 2021 | 30 | 2021 |
| The role of health information in consumers’ willingness to pay for canned crushed tomatoes enriched with Lycopene F Verneau, F La Barbera, M Furno Nutrients 11 (9), 2173, 2019 | 28 | 2019 |
| Variety seeking behavior in the wine domain: A consumers segmentation using big data F Caracciolo, M Furno, M D'Amico, G Califano, G Di Vita Food Quality and Preference 97, 104481, 2022 | 27 | 2022 |
| Parameter instability in quantile regression M Furno Statistical Modelling 7 (4), 345-362, 2007 | 27 | 2007 |
| A robust heteroskedasticity consistent covariance matrix estimator M Furno Statistics: A Journal of Theoretical and Applied Statistics 30 (3), 201-219, 1997 | 24 | 1997 |
| Small sample behavior of a robust heteroskedasticity consistent covariance matrix estimator M Furno Journal of Statistical Computation and Simulation 54 (1-3), 115-128, 1996 | 22 | 1996 |
| The Glejser test and the median regression M Furno Sankhyā: The Indian Journal of Statistics, 335-358, 2005 | 18 | 2005 |
| Analysing the consumer purchasing behaviour for certified wood products in Italy T Panico, F Caracciolo, M Furno Forest Policy and Economics 136, 102670, 2022 | 17 | 2022 |
| Returns to education and gender gap M Furno International Review of Applied Economics 28 (5), 628-649, 2014 | 17 | 2014 |
| Hedonic functions, heterogeneous consumers, and wine market segmentation F Caracciolo, M Furno Journal of Agricultural and Resource Economics 45 (2), 299-316, 2020 | 16 | 2020 |
| Assessing hypothetical bias: An analysis beyond the mean of functional food M Furno, F Verneau, G Sannino Food quality and preference 50, 15-26, 2016 | 16 | 2016 |
| Tests for structural break in quantile regressions M Furno AStA Advances in Statistical Analysis 96, 493-515, 2012 | 16 | 2012 |
| LM tests in the presence of non-normal error distributions M Furno Econometric Theory 16 (2), 249-261, 2000 | 16 | 2000 |
| ARCH tests and quantile regressions M Furno Journal of Statistical Computation and Simulation 74 (4), 277-292, 2004 | 15 | 2004 |
| Analyzing the stability of demand-for-money equations via bounded-influence estimation techniques CF Baum, M Furno Journal of Money, Credit and Banking 22 (4), 465-477, 1990 | 14 | 1990 |
| Beyond one-size-fits-all: Consumers react differently to packaging colors and names of cultured meat in Italy G Califano, M Furno, F Caracciolo Appetite 182, 106434, 2023 | 13 | 2023 |
| Organic consumers’ profile beyond the mean M Furno, T Del Giudice, G Cicia Organic Agriculture, 1-13, 2021 | 11 | 2021 |
