Marialuisa Restaino

Dr. Marialuisa Restaino

Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132-84084 Fisciano (Salerno), Italy
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Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2015 in International Review of Economics & Finance
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Journal Article
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Alessandra AmendolaAlessandra Amendola
Alessandra Amendola
Marialuisa RestainoMarialuisa Restaino
Marialuisa Restaino
LSLuca Sensini
Luca Sensini

1 February 2020 in Social Indicators Research
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Journal Article
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Marialuisa RestainoMarialuisa Restaino
Marialuisa Restaino
MVMaria Prosperina Vitale
Maria Prosperina Vitale
IPIlaria Primerano
Ilaria Primerano

30 August 2017 in International Journal of Injury Control and Safety Promotion
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Journal Article
Open access
Dimitris PotoglouDimitris Potoglou
Dimitris Potoglou
FCFabio Carlucci
Fabio Carlucci
ACAndrea Cirà
Andrea Cirà
Marialuisa RestainoMarialuisa Restaino
Marialuisa Restaino
This paper reports on the factors associated with non-fatal urban-road accident severity. Data on accidents were gathered from the local traffic police in the City of Palermo, one of the six most populated cities in Italy. Findings from a mixed-effects logistic-regression model suggest that accident severity increases when two young drivers are involved, road traffic conditions are light/normal and when vehicles crash on a two-way road or carriageway. Speeding is more likely to cause slight or serious injury even when compared to a vehicle moving towards the opposite direction of traffic. An accident during the summer is more likely to result in a slight or serious injury than an accident during the winter, which is in line with evidence from Southern Europe and the Middle East. Finally, the severity of non-fatal accident injuries in an urban area of Southern Europe was significantly associated with speeding, the age of the driver and seasonality.

5 October 2016 in Quality & Quantity
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Journal Article
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Alessandra AmendolaAlessandra Amendola
Alessandra Amendola
Marialuisa RestainoMarialuisa Restaino
Marialuisa Restaino

1 March 2011 in Journal of Risk Model Validation
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Journal Article
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Alessandra AmendolaAlessandra Amendola
Alessandra Amendola
Marialuisa RestainoMarialuisa Restaino
Marialuisa Restaino
LSLuca Sensini
Luca Sensini
In this paper we develop statistical models for the prediction of bankruptcy in Italian firms in the limited liability sector..., Original Research, Default risk, Models, variable selection

19 July 2013 in Age and Ageing
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Journal Article
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Marialuisa RestainoMarialuisa Restaino
Marialuisa Restaino
FMFiona E. Matthews
Fiona E. Matthews
TMThais Minett
Thais Minett
EAEmiliano Albanese
Emiliano Albanese
CBCarol Brayne
Carol Brayne
BSBlossom Stephan
Blossom Stephan
Background: being able to identify individuals at high risk of dementia is important for diagnostics and intervention. Currently, there is no standard approach to assessing cognitive function in older aged individuals to best predict incident dementia. Objective: to identify cognitive changes associated with an increased risk of 2-year incident dementia using the Cambridge Cognitive Examination (CAMCOG). Design: longitudinal population representative sample aged 65+ years. Methods: individuals were from the Medical Research Council Cognitive Function and Ageing Study. Classification and Regression Tree analysis was used to detect the optimal cut-off value for the CAMCOG total, subscales and composite memory and non-memory scores, for predicting dementia. Sensitivity and specificity of each cut-off score were assessed. Results: from the 2,053 individuals without dementia at the first assessment, 137 developed dementia at the 2-year follow-up. The results indicate similar discriminative accuracy for incident dementia based on the CAMCOG total, memory subscale and composite scores. However, sensitivity and specificity of cut-off values were generally moderate. Scores on the non-memory subscales generally had high sensitivity but low specificity. Compared with the CAMCOG total score they had significantly lower discriminative accuracy. Conclusion: in a population setting, cut-off scores from the CAMCOG memory subscales predicted dementia with reasonable accuracy. Scores on the non-memory scales have lower accuracy and are not recommend for predicting high-risk cases unless all non-memory subdomain scores are combined. The added value of cognition when assessed using the CAMCOG to other risk factors (e.g. health and genetics) should be tested within a risk prediction framework.