Living in the shadow of occupation: Life satisfaction and positive emotion as protective factors in a group of Palestinian school children

Starting from perspectives on functioning and wellbeing, the aim of the present study was to investigate positive and negative affect, self-perceived life satisfaction and happiness in a group of 74 Palestinian children exposed to political violence. We hypothesized that the children in our sample would display generally satisfactory wellbeing and good functioning in terms of positive affect, life satisfaction and happiness, despite their negative life conditions. Both quantitative and qualitative analysis of the dimensions of participants' wellbeing was carried out. In particular, we explored the ecological dimensions affecting the wellbeing of Palestinian children through content analysis of narratives produced by the group of participants. Positive emotions were found to contribute to wellbeing and life satisfaction in children, acting as protective factors in dealing with daily violence. The children displayed a range of personal resources facilitated above all by the functioning community structure supporting them. Implications for clinical intervention are discussed.     

Non parametric mixture priors based on an exponential random scheme

We propose a general procedure for constructing nonparametric priors for Bayesian inference. Under very general assumptions, the proposed prior selects absolutely continuous distribution functions, hence it can be useful with continuous data. We use the notion ofFeller-type approximation, with a random scheme based on the natural exponential family, in order to construct a large class of distribution functions. We show how one can assign a probability to such a class and discuss the main properties of the proposed prior, namedFeller prior. Feller priors are related to mixture models with unknown number of components or, more generally, to mixtures with unknown weight distribution. Two illustrations relative to the estimation of a density and of a mixing distribution are carried out with respect to well known data-set in order to evaluate the performance of our procedure. Computations are performed using a modified version of an MCMC algorithm which is briefly described.     

Conditionally Reducible Natural Exponential Families and Enriched Conjugate Priors

Consider a standard conjugate family of prior distributions for a vector-parameter indexing an exponential family. Two distinct model parameterizations may well lead to standard conjugate families which are not consistent, i.e. one family cannot be derived from the other by the usual change-of-variable technique. This raises the problem of finding suitable parameterizations that may lead to enriched conjugate families which are more flexible than the traditional ones. The previous remark motivates the definition of a new property for an exponential family, named conditional reducibility. Features of conditionally-reducible natural exponential families are investigated thoroughly. In particular, we relate this new property to the notion of cut, and show that conditionally-reducible families admit a reparameterization in terms of a vector having likelihood-independent components. A general methodology to obtain enriched conjugate distributions for conditionally-reducible families is described in detail, generalizing previous works and more recent contributions in the area. The theory is illustrated with reference to natural exponential families having simple quadratic variance function.     

Fiducial and Confidence Distributions for Real Exponential Families

We develop an easy and direct way to define and compute the fiducial distribution of a real parameter for both continuous and discrete exponential families. Furthermore, such a distribution satisfies the requirements to be considered a confidence distribution. Many examples are provided for models, which, although very simple, are widely used in applications. A characterization of the families for which the fiducial distribution coincides with a Bayesian posterior is given, and the strict connection with Jeffreys prior is shown. Asymptotic expansions of fiducial distributions are obtained without any further assumptions, and again, the relationship with the objective Bayesian analysis is pointed out. Finally, using the Edgeworth expansions, we compare the coverage of the fiducial intervals with that of other common intervals, proving the good behaviour of the former.     

How Do We Assess How Agentic We Are? A Literature Review of Existing Instruments to Evaluate and Measure Individuals' Agency

Social Indicators Research - The importance and centrality of the construct of agency is wellknown amongst social scientists. Yet, there is still little agreement on how this construct should be...     

A note on coherent invariant distributions as non-informative priors for exponential and location-scale families

The problem of finding a non-informative prior distribution for a parameter is approached using the notion of context-invariance. This concept is revisited and discussed with the aim of applying it to finding context-invariant non-informative priors for the one-parameter exponential family (suitably redefined) and the location-scale family. Our approach, carried-out in a finitely-additive framework, generally leads to a class of non-informative priors with respect to any given problem. For most common statistical models such a class does not always contain the corresponding Jeffreys' prior, but does contain the so-called ALI prior by Hartigan.