Research project on advocacy and autism

This article presents the findings of a qualitative research project about the difficulties in accessing advocacy faced by adults labelled as having autism and Asperger's Syndrome in the Northwest of England. It is also an example of partnership working between three organisations. The article examines both the process of team-led (emancipatory) research and the project findings. Seven main themes emerged throughout the research: late diagnosis and lack of service support; bad experiences with systems of care; feelings of 'not belonging' (identity issues); barriers around communication and sensitivity towards individuals; lack of awareness and access to advocacy and rights; difficulties of 'fitting into' what is already available; and interest in developing knowledge around advocacy. Examination of the main themes pointed to a disturbing link between poor service response, episodes of crisis and mental ill health. The Carlisle People First Research Team is made up of 6 researchers who are labelled as having 'learning difficulties' who work in partnership with one other researcher.     

Odd-Burr generalized family of distributions with some applications

We study a new family of continuous distributions with two extra shape parameters called the Burr generalized family of distributions. We investigate the shapes of the density and hazard rate function. We derive explicit expressions for some of its mathematical quantities. The estimation of the model parameters is performed by maximum likelihood. We prove the flexibility of the new family by means of applications to two real data sets. Furthermore, we propose a new extended regression model based on the logarithm of the Burr generalized distribution. This model can be very useful to the analysis of real data and provide more realistic fits than other special regression models.     

The Decomposition of Economic Outcomes as a Result of Changes in Brazil’s Male Age–Education Structure

In Brazil, the age and education compositions of the male labor force is changing with great regional variation. Based on Demographic Census microdata, results indicate that cohort size has a negative impact on earnings, but this effect is decreasing over time. In this study we consider the impact on earnings by age and education, as well as estimated income inequality reduction and racial differentials. Fertility decline and improve regarding educational attainment had a significant influence on the decline of income inequality in the country. Moreover, the non-white population has been experiencing less success in relation to educational achievement, compared to the white population.     

Graphical and Numerical Methods for Detecting Influential Observations in Complex Bingham Data

We describe methods to detect influential observations in a sample of pre-shapes when the underlying distribution is assumed to be complex Bingham. One of these methods is based on Cook's distance, which is derived from the likelihood of the complex Bingham distribution. Other method is related to the tangent space, which is based on the local influence for the multivariate normal distribution. A method to detect outliers is also explained. The application of the methods is illustrated in both a real dataset and a simulated sample.     

Female Labor Supply,Human Capital,and Welfare Reform

We estimate a dynamic model of employment, human capital accumulation—including education, and savings for women in the United Kingdom, exploiting tax and benefit reforms, and use it to analyze the effects of welfare policy. We find substantial elasticities for labor supply and particularly for lone mothers. Returns to experience, which are important in determining the longer‐term effects of policy, increase with education, but experience mainly accumulates when in full‐time employment. Tax credits are welfare improving in the U.K., increase lone‐mother labor supply and marginally reduce educational attainment, but the employment effects do not extend beyond the period of eligibility. Marginal increases in tax credits improve welfare more than equally costly increases in income support or tax cuts.     

Regression models to dependence for exceedance

Extreme Value Theory (EVT) aims to study the tails of probability distributions in order to measure and quantify extreme events of maximum and minimum. In river flow data, an extreme level of a river may be related to the level of a neighboring river that flows into it. In this type of data, it is very common for flooding of a location to have been caused by a very large flow from an affluent river that is tens or hundreds of kilometers from this location. In this sense, an interesting approach is to consider a conditional model for the estimation of a multivariate model. Inspired by this idea, we propose a Bayesian model to describe the dependence of exceedance between rivers, where we considered a conditionally independent structure. In this model, the dependence between rivers is captured by modeling the excess marginally of one river as a consequence of linear functions of the other rivers. The results showed that there is a strong and positive connection between excesses in one river caused by the excesses of the other rivers.     

Mortality among elder abuse victims in rural Malaysia: A two-year population-based descriptive study

Our study aims at describing mortality among reported elder abuse experiences in rural Malaysia. This is a population-based cohort study with a multistage cluster sampling method. Older adults in Kuala Pilah (n = 1,927) were interviewed from November 2013 to May 2014. Mortality was traced after 2 years using the National Registration Department database. Overall, 139 (7.2%) respondents died. Fifteen (9.6%) abuse victims died compared to 124 (7.0%) not abused. Mortality was highest with financial abuse (13%), followed by psychological abuse (10.8%). There was a dose-response relationship between mortality and clustering of abuse: 7%, 7.7%, and 14.0% for no abuse, one type, and two types or more, respectively. Among abuse victims, 40% of deaths had ill-defined causes, 33% were respiratory-related, and 27% had cardiovascular and metabolic origin. Results suggest a link between abuse and mortality. Death proportions varied according to abuse subtypes and gender.     

A Bayesian model for multiple change point to extremes,with application to environmental and financial data

Abrupt changes often occur for environmental and financial time series. Most often, these changes are due to human intervention. Change point analysis is a statistical tool used to analyze sudden changes in observations along the time series. In this paper, we propose a Bayesian model for extreme values for environmental and economic datasets that present a typical change point behavior. The model proposed in this paper addresses the situation in which more than one change point can occur in a time series. By analyzing maxima, the distribution of each regime is a generalized extreme value distribution. In this model, the change points are unknown and considered parameters to be estimated. Simulations of extremes with two change points showed that the proposed algorithm can recover the true values of the parameters, in addition to detecting the true change points in different configurations. Also, the number of change points was a problem to be considered, and the Bayesian estimation can correctly identify the correct number of change points for each application. Environmental and financial data were analyzed and results showed the importance of considering the change point in the data and revealed that this change of regime brought about an increase in the return levels, increasing the number of floods in cities around the rivers. Stock market levels showed the necessity of a model with three different regimes.