Involvement of Ministries of Health in Health Service Coverage Decisions: Is England an Aberrant Case?

This article examines the involvement of ministries of health in making health service coverage decisions in Denmark, England, France and Germany. The study aims to inform debate in England about the feasibility of reducing perceived ministerial and bureaucratic ‘interference’ in decisions affecting the National Health Service, based on interviews with senior government officials and other health system stakeholders. Ministries of health differ in their involvement in health system governance and coverage decisions (‘the benefits package’), reflecting differences in institutional arrangements. In all four countries, organizations at arm's length or independent from government are either involved in providing technical advice to the ministry of health or have been mandated to take these decisions themselves. However, ministries of health occasionally intervene in the decision‐making process or ignore the advice of these organizations. The Department of Health in England is not an aberrant case, at least in relation to coverage decisions. Indeed, ministries of health in Denmark and France play a larger role in making these decisions. Public pressure, often amplified by the media, is a shared reason for ministerial and ministry involvement in all four countries. This dynamic may thus limit the feasibility of attempts to further separate the NHS from both the Department of Health and wider political pressures.     

Bayesian mixture modeling for spatial Poisson process intensities,with applications to extreme value analysis

We propose a method for the analysis of a spatial point pattern, which is assumed to arise as a set of observations from a spatial nonhomogeneous Poisson process. The spatial point pattern is observed in a bounded region, which, for most applications, is taken to be a rectangle in the space where the process is defined. The method is based on modeling a density function, defined on this bounded region, that is directly related with the intensity function of the Poisson process. We develop a flexible nonparametric mixture model for this density using a bivariate Beta distribution for the mixture kernel and a Dirichlet process prior for the mixing distribution. Using posterior simulation methods, we obtain full inference for the intensity function and any other functional of the process that might be of interest. We discuss applications to problems where inference for clustering in the spatial point pattern is of interest. Moreover, we consider applications of the methodology to extreme value analysis problems. We illustrate the modeling approach with three previously published data sets. Two of the data sets are from forestry and consist of locations of trees. The third data set consists of extremes from the Dow Jones index over a period of 1303 days.     

Central quantile subspace

Quantile regression (QR) is becoming increasingly popular due to its relevance in many scientific investigations. There is a great amount of work about linear and nonlinear QR models. Specifically, nonparametric estimation of the conditional quantiles received particular attention, due to its model flexibility. However, nonparametric QR techniques are limited in the number of covariates. Dimension reduction offers a solution to this problem by considering low-dimensional smoothing without specifying any parametric or nonparametric regression relation. The existing dimension reduction techniques focus on the entire conditional distribution. We, on the other hand, turn our attention to dimension reduction techniques for conditional quantiles and introduce a new method for reducing the dimension of the predictor $$\mathbf {X}$$. The novelty of this paper is threefold. We start by considering a single index quantile regression model, which assumes that the conditional quantile depends on $$\mathbf {X}$$ through a single linear combination of the predictors, then extend to a multi-index quantile regression model, and finally, generalize the proposed methodology to any statistical functional of the conditional distribution. The performance of the methodology is demonstrated through simulation examples and real data applications. Our results suggest that this method has a good finite sample performance and often outperforms the existing methods.     

Diasporic youth identities of uncertainty and hope: second-generation Albanian experiences of transnational mobility in an era of economic crisis in Greece

This paper explores various dimensions of ‘gender’ and ‘mobility’ among immigrant youth from a transnational perspective in an era of economic crisis. The extent and parameters of continuity, contestation and change in migrant youth identities are analysed and we suggest that neither gender nor identity are stable categories but are embedded in sociocultural particularities both in the country of residence (Greece) but also in the country of origin (Albania). Through in-depth interviews with 52 participants, all second-generation Albanian immigrants in Greece born to two Albanian parents, the paper addresses youth identification in relation to gendered representations of belonging. The narrative accounts that we have selected and analysed reflect the emotional challenges, constraints and creativity of Albanian youth.     

Insight into Informant Discrepancies Regarding Psychosexual Functioning of Adolescents with and without Autism Spectrum Disorder

The private nature of psychosexual functioning leads adolescents and their parents to have different perspectives, which highlights studying parent–child informant discrepancies in this domain. We investigated informant discrepancy in psychosexual functioning, using the self‐report and parent report versions of the Teen Transition Inventory (TTI), of adolescents with autism spectrum disorder (ASD; 136 parent–child dyads) compared to adolescents from the general population (GP; 70 parent–child dyads). Significantly larger informant discrepancies exist in ASD dyads than GP dyads in most domains of psychosexual functioning, except for Body image, Sexual behavior, and Confidence in the future. It is important to use and pay attention to both informants, as discrepancies are relevant for both research and clinical practice regarding psychosexual functioning.     

Bayesian semiparametric modeling and inference with mixtures of?symmetric distributions

We propose a semiparametric modeling approach for mixtures of symmetric distributions. The mixture model is built from a common symmetric density with different components arising through different location parameters. This structure ensures identifiability for mixture components, which is a key feature of the model as it allows applications to settings where primary interest is inference for the subpopulations comprising the mixture. We focus on the two-component mixture setting and develop a Bayesian model using parametric priors for the location parameters and for the mixture proportion, and a nonparametric prior probability model, based on Dirichlet process mixtures, for the random symmetric density. We present an approach to inference using Markov chain Monte Carlo posterior simulation. The performance of the model is studied with a simulation experiment and through analysis of a rainfall precipitation data set as well as with data on eruptions of the Old Faithful geyser.     

On the improvement of one-sided S control charts

The most common charting procedure used for monitoring the variance of the distribution of a quality characteristic is the S control chart. As a Shewhart-type control chart, it is relatively insensitive in the quick detection of small and moderate shifts in process variance. The performance of the S chart can be improved by supplementing it with runs rules or by varying the sample size and the sampling interval. In this work, we introduce and study one-sided adaptive S control charts, supplemented or not with one powerful runs rule, for detecting increases or decreases in process variation. The properties of the proposed control schemes are obtained by using a Markov chain approach. Furthermore, a practical guidance for the choice of the most suitable control scheme is also provided.     

TEMPTATION‐DRIVEN BEHAVIOR AND TAXATION: A QUANTITATIVE ANALYSIS IN A LIFE‐CYCLE MODEL

This paper examines the impact of labor and capital income taxes in a stochastic overlapping generations (OLG) economy where agents face borrowing constraints and their behavior is temptation driven. We quantitatively establish that the existence of temptation in preferences may function as an opposing mechanism to modeling choices, such as liquidity constraints, life‐cycle structure, and idiosyncratic earnings risks, that are critical in delivering a positive capital income tax rate. We show that a sufficiently large measure of individuals having self‐control preferences, or alternatively, a sufficiently high cost of exercising self‐control, puts downward pressure on the optimal capital income tax rate. (JEL E21, E62, H55)