Diagnostics robustes à des délais individuels en utilisant les estimateurs robustes RA-ARX

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

An integrated approach of data envelopment analysis and boosted generalized linear mixed models for efficiency assessment

Performance evaluation is an important part in the management of any decision-making unit (DMU) as it identifies sources of managerial inefficiencies and provides a policy for inefficient DMUs to improve their efficiency. The latter is generally affected by environmental variables that are beyond managerial control. Modeling the impact of these environmental variables is a critical issue for both researchers and practitioners. Researchers developed and proposed several methods to deal with this issue in general and in the data envelopment analysis (DEA) literature in particular. However, the available two-stage DEA methods do not account for interdependence between observations and they are of limited use when the number of variables is fairly large. This paper proposes an integrated framework combining DEA, and boosted generalized linear mixed models (GLMMs) that accounts for the interdependence problem when studying the impact of environmental variables on performance. Additionally, the framework carries out variable selection. The framework is illustrated with a sample of 151 commercial banks from Middle East and North African countries.Scopu

A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information

The efficiency of banks has a critical role in development of sound financial systems of countries. Data Envelopment Analysis (DEA) has witnessed an increase in popularity for modeling the performance efficiency of banks. Such efficiency depends on the appropriate selection of input and output variables. In literature, no agreement exists on the selection of relevant variables. The disagreement has been an on-going debate among academic experts, and no diagnostic tools exist to identify variable misspecifications. A cognitive analytics management framework is proposed using three processes to address misspecifications. The cognitive process conducts an extensive review to identify the most common set of variables. The analytics process integrates a random forest method; a simulation method with a DEA measurement feedback; and Shannon Entropy to select the best DEA model and its relevant variables. Finally, a management process discusses the managerial insights to manage performance and impacts. A sample of data is collected on 303 top-world banks for the periods 2013 to 2015 from 49 countries. The experimental simulation results identified the best DEA model along with its associated variables, and addressed the misclassification of the total deposits. The paper concludes with the limitations and future research directions.- The National Research Center of Lebanon. - The University Research Board of the American University of Beiru

Personality traits and high cigarette dependence among university students: Insights from Lebanon.

PurposeThe use of tobacco and cigarette products remains widespread globally, with varying patterns across countries. Understanding the factors influencing cigarette dependence among young adults is crucial for effective smoking prevention and control programs. Personality traits are one of the factors that influence smoking behaviour, yet the evidence on their role in high cigarette dependence among young adults remains inconclusive. This study aims to provide insights and initial evidence on the potential association between personality dimensions, sociodemographic factors, lifestyle habits, and high cigarette dependence among Lebanese university students.MethodsA convenient sample of 212 student smokers from one private and one public university in Lebanon participated in an online survey. The survey included measures of personality traits using the Big-Five framework, sociodemographic factors, lifestyle habits, and the Fagerström Test for Cigarette Dependence (FTCD). Logistic regression models and mediation analysis were used to analyze the data.ResultsThe results revealed significant associations between personality dimensions and high cigarette dependence among Lebanese university students. Smokers with higher levels of Openness to Experience were more likely to have high cigarette dependence (β = 0.408, p ConclusionThese findings highlight the importance of considering personality dimensions, sociodemographic factors, and lifestyle habits in understanding high cigarette dependence among Lebanese university students. The results can inform the development of targeted interventions to address high cigarette dependence in this population

Logistic regression model for exploring impact of personality dimensions on cigarette dependence after adjusting for sociodemographic and lifestyle variables.

Logistic regression model for exploring impact of personality dimensions on cigarette dependence after adjusting for sociodemographic and lifestyle variables.</p

Indirect effect(s) of openness to experience on cigarette dependence.

Indirect effect(s) of openness to experience on cigarette dependence.</p

Logistic regression output for exploring impact of personality dimensions on cigarette dependence.

Logistic regression output for exploring impact of personality dimensions on cigarette dependence.</p

Demographic and lifestyle characteristics of study participants.

Demographic and lifestyle characteristics of study participants.</p