Historic Centers and Urban Quality: a Study Concerning Perceived Needs and Expectations

This paper proposes an econometric approach based on Discrete Choice Models to identify and analyze the residents’ needs and expectations concerning the spatial organization of the historic neighborhoods where they live, which provides inferences on residential satisfaction’s determinants. In this framework, determinants are grouped into three distinct categories as follows: i. level of satisfaction related to house; ii. neighborhood characteristics; iii. respondents’ social and demographic characteristics. The information coming from the implementation of the DCM-based analysis can be used as an important reference point for the definition of planning policies for historic centers’ preservation. In order to test and discuss its effectiveness, we use the model to analyze needs and expectations of the residents of the historic center of Cagliari, a medium-sized urban context of the Italian insular region of Sardinia

Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards

In the presence of competing risks, the estimation of crude cumulative incidence, i.e. the probability of a specific failure as time progresses, has received much attention in the methodological literature. It is possible to estimate crude cumulative incidence starting from models defined on cause-specific hazards or to adopt regression strategies modeling directly the quantity of interest. A generalized linear model based on discrete cause-specific hazard is used to obtain the crude cumulative incidence and its asymptotic variance. The model allows inference both on cause-specific hazard and on crude cumulative incidence in the presence of time dependent effects. Standard software can be used to compute all quantities of interest. A trial of chemoprevention of leukoplakia is considered for illustrative purposes, where different patterns of risk are suspected for the different causes of treatment failure

Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards

In the presence of competing risks, the estimation of crude cumulative incidence, i.e.the probability of a specific failure as time progresses, has received much attention in the methodological literature. It is possible to estimate crude cumulative incidence starting from models defined on cause-specific hazards or to adopt regression strategies modeling directly the quantity of interest. A generalized linear model based on discrete cause-specific hazard is used to obtain the crude cumulative incidence and its asymptotic variance. The model allows inference both on cause-specific hazard and on crude cumulative incidence in the presence of time dependent effects. Standard software can be used to compute all quantities of interest. A trial of chemoprevention of leukoplakia is considered for illustrative purposes, where different patterns of risk are suspected for the different causes of treatment failure.