The unit extended Weibull families of distributions and its applications

In this paper, two new general families of distributions supported on the unit interval are introduced. The proposed families include several known models as special cases and define at least twenty (each one) new special models. Since the list of well-being indicators may include several double bounded random variables, the applicability for modeling those is the major practical motivation for introducing the distributions on those families. We propose a parametrization of the new families in terms of the median and develop a shiny application to provide interactive density shape illustrations for some special cases. Various properties of the introduced families are studied. Some special models in the new families are discussed. In particular, the complementary unit Weibull distribution is studied in some detail. The method of maximum likelihood for estimating the model parameters is discussed. An extensive Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples. Applications to the literacy rate in Brazilian and Colombian municipalities illustrate the usefulness of the two new families for modeling well-being indicators.     

A Note on Nonlinear Cointegration,Misspecification, and Bimodality

We derive the asymptotic distribution of the ordinary least squares estimator in a regression with cointegrated variables under misspecification and/or nonlinearity in the regressors. We show that, under some circumstances, the order of convergence of the estimator changes and the asymptotic distribution is non-standard. The t-statistic might also diverge. A simple case arises when the intercept is erroneously omitted from the estimated model or in nonlinear-in-variables models with endogenous regressors. In the latter case, a solution is to use an instrumental variable estimator. The core results in this paper also generalise to more complicated nonlinear models involving integrated time series.     

Guns and Suicides

There is a consensus in the literature that the ratio of suicides committed with guns to total suicides is the best indirect measure of gun ownership. However, such a proxy is not accurate for any locality with low population density in view that suicides are rare events. To circumvent this issue, we exploit the socioeconomic characteristics of the suicide victims in order to come up with a novel proxy for gun ownership. We assess our indicator using suicide micro-data from the Brazilian Ministry of Health between 2000 and 2010.     

Disability insurance risks: The Argentinian case

This article analyses the risk of disability facing workers who contribute to the Argentinian Integrated Social Security System (Sistema Integrado Previsional Argentino— SIPA). Using administrative records as our source of data for the period 2000‐2006, the results indicate that 1.46 workers per 1,000 became disabled annually during that period. The risk of disability rates were higher for men than for women, but increased with age for both sexes. The risk of disability rates have also been broken down by pathology and social security scheme, taking the effects of age and sex into account. To conclude, international comparisons are presented.     

The Rich and the Poor: The Construction of an Affluence Line from the Poverty Line

The paper proposes a simple methodology to estimate an affluence line that depends on the knowledge of the income distribution and the poverty line for a given population. The idea that poverty is morally unacceptable and can be eradicated through redistribution of wealth provides the grounds for the methodology. The line is defined as the value that delimitates the aggregated income required to eradicate poverty by the way of transfers from the rich to the poor. I estimate an affluence line using Brazilian 1999 National Household Survey data and briefly discuss the results.     

New results on the Ristić–Balakrishnan family of distributions

ABSTRACT

Adding new shape parameters to expand a model into a larger family of distributions to provide significantly skewed and heavy-tails plays a fundamental role in distribution theory. For any continuous baseline G distribution, Risti? and Balakrishnan (2012 Risti?, M.M., Balakrishnan, N. (2012). The gamma exponentiated exponential distribution. J. Stat. Comput. Simul. 82:1191–1206.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) proposed the gamma-generated family of distributions with an extra positive shape parameter. They presented some special models of their family but did not study its properties. This paper examines some general mathematical properties of this family which hold for any baseline model. Some distributions are studied and a number of existing results in the literature can be recovered as special cases. We estimate the model parameters by maximum likelihood and illustrate the importance of the family by means of an application to a real data set.     

A framework for supporting health capability-based planning: Identifying and structuring health capabilities

The COVID-19 pandemic has highlighted that health security systems must be redesigned, in a way that they are better prepared and ready to cope with multiple and diverse health threats, from predictable and well-known epidemics to unexpected and challenging pandemics. A powerful way of accomplishing this goal is to focus the planning on health capabilities. This focus may enhance the ability to respond to and recover from health threats and emergencies, while helping to identify the level of resources required to maintain and build up those capabilities that are critical in ensuring the preparedness of health security systems. However, current attempts for defining and organizing health capabilities have some important limitations. First, such attempts were not designed to consider diverse scenarios and multiple health threats. Second, they provide a limited representation of capabilities and lack a systemic perspective. Third, they struggle to identify capability and resource gaps. In this article, we thus propose a new framework for identifying and structuring health capabilities and support health capability planning. The suggested framework has three main potential benefits. First, the framework may help policymakers in planning under high levels of uncertainty, by considering multiple realistic and stressful scenarios. Second, it can provide risk analysts with a more comprehensive representation of health capabilities and their complex relationships. Third, it can support planners in identifying resource and capability gaps. We illustrate the use of the framework in practice considering an outbreak scenario caused by three different health threats (COVID-19, Ebola, and Influenza viruses).