1,721,075 research outputs found
Ask me if I am happy: sport practice and life satisfaction in Italy
No full textThis paper investigates the influence of physical activity on well-being. We use data from the Aspects of Daily Life survey provided by the Italian National Institute of Statistics, for the period 2013–2019. To capture the effect of sport participation on life satisfaction we face the problem of endogeneity. We solve this issue from a methodological point of view by using an instrumental variable (IV) ordered probit model, where the instrument has been identified in living nearby to an equipped green area. Our investigation suggests that physical activity is positively and strongly associated with life satisfaction, showing that the probability of being very happy is very high (70%) among individuals practicing sport regularly. For this reason, policies targeted at promoting and supporting sport practice play a key role in determining the subjective well-being of citizens. Our results also indicate a significant association between life satisfaction and gender, and between life satisfaction and inability to work, revealing the need to foster inclusive policies to reduce differences in well-being among groups of population
Bayes estimators of log-normal means with finite quadratic expected loss
Full text linkThe log-normal distribution is a popular model in biostatistics as in many other fields of statistics. Bayesian inference on the mean and median of the distribution is problematic because, for many popular choices of the prior for variance (on the log-scale) parameter, the posterior distribution has no finite moments, leading to Bayes estimators with infinite expected loss for the most common choices of the loss function. In this paper we propose a generalized inverse Gaussian prior for the variance parameter, that leads to a log-generalized hyperbolic posterior, a distribution for which it is easy to calculate quantiles and moments, provided that they exist. We derive the constraints on the prior parameters that yields finite posterior moments of order r. For the quadratic and relative quadratic loss functions, we investigate the choice of prior parameters leading to Bayes estimators with optimal frequentist mean square error. For the estimation of the lognormal mean we show, using simulation, that the Bayes estimator under quadratic loss compares favorably in terms of frequentist mean square error to known estimators. The theory does not apply only to the mean or median estimation but to all parameters that may be written as the exponential of a linear combination of the distribution's two
Work history patterns under different arrangements. A case study for the Italian labour market
No full textBayesian cure-models with a logistic reparameterization, with application to Italian labor market analysis
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Robust Bayesian small area estimation based on quantile regression
No full textQuantile and M-quantile regression have been applied successfully to small area estimation within the frequentist approach. Quantile regression is applied in the same context but from a Bayesian perspective. Joint modelling of the quantile function is considered, adopting a non parametric assumption on the data generating process that nonetheless explicitly includes the normal distribution as a special case. A specification of the random part of the model that is simple and consistent with the predictive aim of small area estimation is proposed. Although the main output of the method is the estimation of the whole quantile function, estimators of the small area means based on the integration of the quantile function are proposed and discussed. A simulation exercise is used to assess the frequentist properties of these proposed predictors, that result at least as efficient as frequentist small area estimators based on quantile regression in scenarios characterized by the presence of outliers. The proposed method is illustrated using data from the European survey on Income and Living Conditions (EU-SILC)
Bayesian inference for quantiles of the log-normal distribution
No full textThe log-normal distribution is very popular for modeling positive right-skewed data and represents a common distributional assumption in many environmental applications. Here we consider the estimation of quantiles of this distribution from a Bayesian perspective. We show that the prior on the variance of the log of the variable is relevant for the properties of the posterior distribution of quantiles. Popular choices for this prior, such as the inverse gamma, lead to posteriors without finite moments. We propose the generalized inverse Gaussian and show that a restriction on the choice of one of its parameters guarantees the existence of posterior moments up to a prespecified order. In small samples, a careful choice of the prior parameters leads to point and interval estimators of the quantiles with good frequentist properties, outperforming those currently suggested by the frequentist literature. Finally, two real examples from environmental monitoring and occupational health frameworks highlight the improvements of our methodology, especially in a small sample situation
The over-shrinkage effect of EBLUP and M-quantile: problems for the estimation of Small Area Parameters
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