Mariangela Zenga

Maximum likelihood estimation in Dagum distribution with censored samples

In this work, we show that the Dagum distribution [3] may be a competitive model for describing data which include censored observations in lifetime and reliability problems. Maximum likelihood estimates of the three parameters of the Dagum distribution are determined from samples with type I right and type II doubly censored data. We perform an empirical analysis using published censored data sets: in certain cases, the Dagum distribution fits the data better than other parametric distributions that are more commonly used in survival and reliability analysis. Graphical comparisons confirm that the Dagum model behaves better than a number of competitive distributions in describing the empirical hazard rate of the analyzed data. A probability plot to provide graphical check of the appropriateness of the Dagum model for right censored data is constructed, and the details are given in the appendix. Finally, a simulation study that shows the good performance of the maximum likelihood estimators of the Dagum shape parameters for finite type II doubly censored samples is carried out.

Simulating Coxian phase‐type distributions for patient survival

Coxian phase-type distributions are a special type of Markov model that can be used to represent survival times in terms of phases through which an individual may progress until they eventually leave the system completely. Previous research has considered the Coxian phase-type distribution to be ideal in representing patient survival in hospital. However, problems exist in fitting the distributions. This paper investigates the problems that arise with the fitting process by simulating various Coxian phase-type models for the representation of patient survival and examining the estimated parameter values and eigenvalues obtained. The results indicate that numerical methods previously used for fitting the model parameters do not always converge. An alternative technique is therefore considered. All methods are influenced by the choice of initial parameter values. The investigation uses a data set of 1439 elderly patients and models their survival time, the length of time they spend in a UK hospital.

Departures from the formal of actual students' university careers: an application of non-homogeneous fuzzy Markov chains

As in most higher education (HE) systems, the Italian university organisation draws paths of credit progression in formal curricula, which aim at framing the acquisition of knowledge and competencies within each specific major. The resulting yearly syllabi therefore develop in a sequence of examinations that are to be successfully passed, and formal administrative registration allows access to the following academic year. In general, there is a divergence between formal and actual career progression because each university student can proceed at her/his own pace, sketching her/his own trajectories, free to depart from the formal progression. Even if applied to various HE settings, Markov chain models do not fit the aforementioned situation. A methodological extension has been introduced, whereby progression levels are considered as fuzzy states. Markov chains with fuzzy states identify the latter with specified academic years and express each students situation as a relational link to present and past academic attainments. This link is operationalised by means of a membership function, which is here discussed with reference to the Italian HE system.

Different approaches to the pursuit of internationalization by Italian SMEs

Purpose – The aim of this paper is to analyse why Italian small and medium enterprises (SMEs) pursue internationalization (current and future entry modes, motivations, advantages and difficulties) and how they go about it, with reference to four key areas: innovation and technology, networking, environmental approach and human resource (HR) competences. Design/methodology/approach – A questionnaire was distributed to 792 enterprises with a response rate of 24.37 per cent. Data were collected using the computer assisted web interviewing (CAWI) method and processed with Rasch analysis, Principal Components Analysis and Cluster analysis methods. Findings – The paper presents the results of a quantitative research on SMEs located in the Province of Monza and Brianza – one of the most productive territories in Italy. Four different clusters emerged with specific approaches. Briefly, this paper points out that: innovation is mostly linked to the product and is incremental; HR and their competences are crucial for facing complex markets; the green issue is not dominant (it is considered only for saving energy and reducing cost production); and networking is not a key issue (except informal relations, contractual agreements and strategic alliances). Research limitations/implications – The research could be extended: through a longitudinal survey on the same sample; by covering different territories on the same topics. The cluster analysis identifies potential guidelines for entrepreneurial behaviour in respect to key factors for exiting from the economic and financial crisis: innovation and technology, formal and informal networks, the “green” approach, HR training. Originality/value – This paper presents a new interdisciplinary approach that may work beyond country boundaries, providing a new basis to the debate on the internationalization of SMEs.

Modelling the Length of Stay of Geriatric Patients in Emilia Romagna Hospitals Using Coxian Phase-Type Distributions with Covariates

The attention placed on healthcare systems has been constantly increasing in recent years. This is especially true for geriatric services: older people often have complex medical and social needs and the proportion of elderly in the population is currently rising. In this paper we apply the Coxian phase-type distribution to model the length of stay of geriatric patients admitted to 19 geriatric wards at hospitals in the Emilia-Romagna region in Italy for the years 2008–2011. The results confirm previous research carried out on patients in the UK and extends the research by allowing the influence of patient characteristics, available on admission, to be taken into account as covariates.

Predicting elderly patient length of stay in hospital and community care using a series of conditional Coxian phase-type distributions, further conditioned on a survival tree

Increasing demand on hospital resources by an ageing population is impacting significantly on the number of beds available and, in turn, the length of time that elderly patients must wait for a bed before being admitted to hospital. This research presents a new methodology that models patient pathways and allows the accurate prediction of patient length of stay in hospital, using a phase-type survival tree to cluster patients based on their covariates and length of stay in hospital. A type of Markov model, called the conditional Coxian phase-type distribution is then implemented, with the probability density function for the time spent at a particular stage of care, for example, the first community discharge, conditioned on the length of stay experienced at the previous stage, namely the initial hospital admission. This component of the methodology is subsequently applied to each cohort of patients over a number of hospital and community stages in order to build up the profile of patient readmissions and associated timescales for each cohort. It is then possible to invert the methodology, so that the length of stay for an observation representing a new patient admission may be estimated at each stage of care, based on the assigned cohort at the initial hospital stage. This approach provides hospital managers with an accurate understanding of the rates with which different groups of patients move between hospital and community care, which may be used to reduce the negative effects of bed-blocking and the premature discharge of patients without a required period of convalescence. This has the benefit of assisting hospital managers with the effective allocation of vital healthcare resources. The approach presented is different to previous research in that it allows the inclusion of patient covariate information into the methodology describing patient transitions between hospital and community care stages in an aggregate Markov process. A data set containing hospital readmission data for elderly patients from the Abruzzo region of Italy is used as a case study in the application of the presented methodology.

A Discrete Conditional Phase-Type Model Utilising a Survival Tree for the Identification of Elderly Patient Cohorts and Their Subsequent Prediction of Length of Stay in Hospital

Health care providers continue to feel the pressure in providing adequate care for an increasing elderly population. If length of stay patterns for elderly patients in care can be captured through analytical modelling, then accurate predictions may be made on when they are expected to leave hospital. The Discrete Conditional Phase-type (DC-Ph) model is an effective technique through which length of stay in hospital can be modelled and consists of both a conditional and a process component. This research expands the DC-Ph model by introducing a survival tree as the conditional component, whereby covariates are used to partition patients into cohorts based on their distribution of length of stay in hospital. The Coxian phase-type distribution is then used to model the length of stay for patients belonging to each cohort. A demonstration of how patient length of stay may be predicted for new admissions using this methodology is then given. This tool has the benefit of providing an aid to the decision making processes undertaken by hospital managers and has the potential to result in the more effective allocation of hospital resources. Hospital admission data from the Lombardy region of Italy is used as a case-study.

Financial Literacy and Undergraduates: A Question of Aptitude?

Our study tests whether differing profiles of students - measured according to standard socio-demographic characteristics and their financial aptitude - show different levels of financial literacy at the beginning of their university careers. It is widely acknowledged that financial literacy among the young is influenced by socio-demographic characteristics. Levering on the results of studies estimating the influence of genetic factors on financial behavior, we argue that along with demographics and experience there are unobservable variables, such as aptitude, that help explain a student’s financial literacy. We surveyed 366 Business Studies freshmen during their first few weeks at a large Italian university: in other words, our sample is composed of freshmen with no prior educational exposure to financial matters except, in some cases, a high school diploma in commercial studies or a personal interest in financial issues. After controlling for education, gender, work and financial experience, parents’ educational attainment, students of Finance showed a higher level of financial literacy with respect to their peers: thus confirming the role of financial aptitude

Consumer perceptions towards sustainable supply chain practices in the hospitality industry

ABSTRACT This study investigates the impacts of economic, social and environmental sustainability practices of companies in the hospitality supply chain on consumers’ satisfaction, loyalty and willingness to pay higher prices. Utilizing data collected from 288 tourists visiting south Sardinia, the study indicates that while economic sustainability practices have positive impacts on consumers’ satisfaction, loyalty and willingness to pay a premium, sustainability practices related to environmental and social dimensions have a direct positive impact on satisfaction and an indirect positive impact on consumer loyalty and willingness to pay a premium. Additionally, findings reveal that satisfaction is likely to mediate the impact of environmental and social sustainability practices on the loyalty of consumers. The theoretical and managerial implications of the study are provided.

Strategic choices in recessionary period: an exploration on italian smes

From the 2012 survey of the Osservatorio Impresa Monza e Brianza we shed some light on the behavior and strategic choices of micro, small and medium sized enterprises to cope with the global financial and economic crisis. The analysis was conducted with the aim of discovering which strategies are more likely to be implemented in the short-term using the Rasch model for multinomial ordinal response categories. Moreover, we present a segmentation based on the enterprises’ reactivity towards the crisis with respect to the effectiveness of crisis measures and the main characteristics of the enterprises.