Bayes estimators of log-normal means with finite quadratic expected loss

The 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

Disability and work intensity in Italian households

The 2030 Agenda of the United Nations clearly sets the inclusion of persons with disabilities in the labour market as a main goal. However, especially in care welfare systems characterized by a low level of social services, disability not only impacts the labour market participation of disabled people themselves but may also affect the labour opportunities of other members of their household. Using EU-SILC data to compute individual work intensity-as a better measure of the actual level of labour attainment-this paper aims to disentangle direct and indirect correlations between disability and labour market participation in Italian households. In confirming the negative direct correlation between disability and labour market participation, the results also show a negative indirect correlation that depends on the family relationship between the disabled person and household members

Mapping poverty at multiple geographical scales

Poverty mapping is a powerful tool to study the geography of poverty. The choice of the spatial resolution is central as poverty measures defined at a coarser level may mask their heterogeneity at finer levels. We introduce a small area multi-scale approach integrating survey and remote sensing data that leverages information at different spatial resolutions and accounts for hierarchical dependencies, preserving estimates coherence. We map poverty rates by proposing a Bayesian Beta-based model equipped with a new benchmarking algorithm that accounts for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.Comment: 22 pages, 7 figure

Comparing Material and Social Deprivation Indicators: Identification of Deprived Populations

The new indicator for material and social deprivation validated in 2014 by the European Commission enlarged the scope of measuring social exclusion, which entails both material hardship of individuals and households, and a relevant social dimension. Using EU-SILC data, this paper compares the standard measure of material deprivation and the new indicator in terms of the sub-population they identify as suffering deprivation across Europe. In 2019, only 57% of the deprived individuals according at least one of the two indicators were so according to both, while 23% was deprived only according to the new measure and 20% was deprived only under the old indicator. We compare the micro-level determinants of inclusion into these different deprived populations, both at the aggregated level and separately for each of the 21 countries included in our sample

Estimation and Testing in M-quantile Regression with Applications to Small Area Estimation

In recent years,M-quantile regression has been applied to small area estimation to obtain reliable and outlier robust estimators without recourse to strong parametric assumptions. In this paper, after a review of M-quantile regression and its application to small area estimation, we cover several topics related to model specification and selection for M-quantile regression that received little attention so far. Specifically, a pseudo-R2 goodness-of-fit measure is proposed, along with likelihood ratio and Wald type tests for model specification. A test to assess the presence of actual area heterogeneity in the data is also proposed. Finally, we introduce a new estimator of the scale of the regression residuals, motivated by a representation of the M-quantile regression estimation as a regression model with Generalised Asymmetric Least Informative distributed error terms. The Generalised Asymmetric Least Informative distribution, introduced in this paper, generalises the asymmetric Laplace distribution often associated to quantile regression. As the testing procedures discussed in the paper are motivated asymptotically, their finite sample properties are empirically assessed in Monte Carlo simulations. Although the proposed methods apply generally to Mquantile regression, in this paper, their use ar illustrated by means of an application to Small Area Estimation using a well known real dataset

Extended beta models for poverty mapping. An application integrating survey and remote sensing data in Bangladesh

The paper targets the estimation of the poverty rate at the Upazila level in Bangladesh through the use of Demographic and Health Survey (DHS) data. Upazilas are administrative regions equivalent to counties or boroughs whose sample sizes are not large enough to provide reliable estimates or are even absent. We tackle this issue by proposing a small area estimation model complementing survey data with remote sensing information at the area level. We specify an Extended Beta mixed regression model within the Bayesian framework, allowing it to accommodate the peculiarities of sample data and to predict out-of-sample rates. In particular, it enables to include estimates equal to either 0 or 1 and to model the strong intra-cluster correlation. We aim at proposing a method that can be implemented by statistical offices as a routine. In this spirit, we consider a regularizing prior for coefficients rather than a model selection approach, to deal with a large number of auxiliary variables. We compare our methods with existing alternatives using a design-based simulation exercise and illustrate its potential with the motivating application

Dimethyl carbonate: an environmentally friendly solvent for hydrogen peroxide (H2O2)/methyltrioxorhenium (CH3ReO3, MTO) catalytic oxidations

Environmentally friendly oxidations of various organic compounds with the hydrogen peroxide (H2O2)/methyltrioxorhenium(CH3ReO3, MTO) catalytic system have been described in dimethyl carbonate (DMC), a cheap commercially available and benign chemical having interesting solvating properties, low toxicity and high biodegradability. Oxidations proceeded with good conversions and in good yields. Spectrophotometric analysis demonstrated that the [CH3ReO(O–O)2] complex was formed in DMC and that it was stable for several days at room temperature.L'articolo è disponibile sul sito dell'editore: http://www.sciencedirect.co