A class of bivariate regression models for discrete and/or continuous responses

ABSTRACT

A general class of models for discrete and/or continuous responses is proposed in which joint distributions are constructed via the conditional approach. It is assumed that the distributions of one response and of the other response given the first one belong to exponential family of distributions. Furthermore, the marginal means are related to the covariates by link functions and a dependency structure between the responses is inserted into the model. Estimation methods, diagnostic analysis and a simulation study considering a Bernoulli-exponential model, a particular case of the class, are presented. Finally, this model is used in a real data set.     

Strong consistency of presmoothed Kaplan–Meier integrals when covariables are present

It is known that the Kaplan–Meier estimation may be improved via presmoothing methods. In this article, we introduce an extended presmoothed Kaplan–Meier estimator in the presence of covariates. The main result is the strong consistency of general empirical integrals based on such an estimator. As applications, one can obtain a consis-tent multivariate empirical distribution under censoring, and also can obtain a consistent estimation of regression parameters. We illustrate the new estimation methods through simulations and real data analysis.     

Bayesian factor models in characterizing molecular adaptation

Assessing the selective influence of amino acid properties is important in understanding evolution at the molecular level. A collection of methods and models has been developed in recent years to determine if amino acid sites in a given DNA sequence alignment display substitutions that are altering or conserving a prespecified set of amino acid properties. Residues showing an elevated number of substitutions that favorably alter a physicochemical property are considered targets of positive natural selection. Such approaches usually perform independent analyses for each amino acid property under consideration, without taking into account the fact that some of the properties may be highly correlated. We propose a Bayesian hierarchical regression model with latent factor structure that allows us to determine which sites display substitutions that conserve or radically change a set of amino acid properties, while accounting for the correlation structure that may be present across such properties. We illustrate our approach by analyzing simulated data sets and an alignment of lysin sperm DNA.     

On time-irreversibility and other non-linear features in time series

Time-irreversibility, asymmetry of the distribution, and the occurrence of sudden bursts are considered, amongst others, as non-linear features in time series modeling. The implication is often made that time series showing these features must be analyzed using non-linear models. In contrast, this paper shows that time-irreversible asymmetric time series showing certain types of sudden bursts may be generated by linear models with adequate input sequences. Thus some non-linear time series features may be caused by the pattern in the input sequence rather than by non-linearity in the model. Examples are considered to illustrate the situation.     

Bayesian influence diagnostics in radiocarbon dating

Linear models constitute the primary statistical technique for any experimental science. A major topic in this area is the detection of influential subsets of data, that is, of observations that are influential in terms of their effect on the estimation of parameters in linear regression or of the total population parameters. Numerous studies exist on radiocarbon dating which propose a value consensus and remove possible outliers after the corresponding testing. An influence analysis for the value consensus from a Bayesian perspective is developed in this article.     

‘Making narrative portraits: a methodological approach to analysing qualitative data’

ABSTRACT

This paper proposes the method of ‘narrative portraiture’, which, located within the wider field of narrative studies, offers an analytical tool to narrative data. Two research projects, one on disability and one on identity, are used to illustrate how the method can be applied. While the paper will focus on the methodological benefits and limitations of the approach, throughout the article we also highlight the ethical concern of representation. We suggest that through ‘narrative portraiture’ research findings can be contextualised in broader social narratives without losing sight of the unique personal qualities of the research encounter. Thus, we argue for the importance of bringing the participant and their everyday life experience into focus, highlighting that a portrayal of a sole story can be, not only a medium to understand a research phenomenon, but also a valuable research output in itself.     

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.     

Measuring and Analysing Community Action for Health: An Indicator-Based Typology and Its Application to the Case of Barcelona

Social welfare systems face major challenges, particularly in a context of social transformation, austerity and growing inequalities. This process is highly visible in the health sector. In this context, many voices ask for public sector reforms and community action for health as a relevant practice. However, analyses and evaluations of this kind of practices are still limited, particularly beyond the cases of single community health actions or interventions. We still need to identify key indicators for measuring and characterising what community action for health consists of, as well as to what degree this kind of intervention has been developed across a city. Based on a research about 49 neighborhoods in Barcelona, this paper creates an index to measure and characterize community action for health, using different indicators: citizen engagement programs in community health, organizational transformation of the health and social protection systems, stable participatory structures with specific teams, and urban health policies. We apply the index to the case of Barcelona and build a map of community action for health in the city using 4 categories: strong community health development (one neighbourhood), middle (9 neighborhoods), emergent (25 neighborhoods) and without specific community health promotion (14 neighbourhoods). We find that community action for health is extensive within the city of Barcelona, have great potential as a response to the need for change in the relationship between the public (health) sector and the citizenry, but is still implemented unequally across the urban territory in terms of types and methods.     

The Role of Survey Data in Microsimulation Models for Social Policy Analysis

Questions about the impact of social policies cannot be answered solely on the basis of official statistics made available in tabular format. The access to microdata allows policy analysts to answer these questions with a larger set of analytical tools, in particular microsimulation models. This paper examines the interface between survey data and microsimulation models. We review different types of microsimulation models (static and dynamic) and their data requirements. We then restrict the attention to survey-based microdata, and examine issues in survey design, questionnaire content, data quality, and dissemination policy that are important from the perspective of microsimulation.