Bayesian networks for imputation

Summary.  Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables ( statistical consistency ) and preservation of logical constraints in data ( logical consistency ). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.     

The evaluation of DNA evidence in pedigrees requiring population inference

Summary. The evaluation of nuclear DNA evidence for identification purposes is performed here taking account of the uncertainty about population parameters. Graphical models are used to detail the hypotheses being debated in a trial with the aim of obtaining a directed acyclic graph. Graphs also clarify the set of evidence that contributes to population inferences and they also describe the conditional independence structure of DNA evidence. Numerical illustrations are provided by re-examining three case-studies taken from the literature. Our calculations of the weight of evidence differ from those given by the authors of case-studies in that they reveal more conservative values.     

Displaying Gender: Transgender People's Strategies in Everyday Life

Drawing upon data from in‐depth interviews with transgender people in Portugal and in the United Kingdom, this article critically reflects upon different ways through which transgender people recreate their gender displays, particularly in terms of gender embodiment and aesthetics, in order to navigate their everyday lives. I focus on four strategies of articulating gender displays with one's own gender subjectivities: “blending in,” “masking,” “naturalizing,” and “subverting.” These strategies are not exclusive of one another. They are used differently throughout the participants' own lives in accordance to the social contexts they were in.     

The Examination of Emotional Facial Expressions Within Parent–Child and Sibling Interactive Contexts: A Systematic Review

This work constitutes a systematic review of the empirical literature about emotional facial expressions displayed in the context of family interaction. Searches of electronic databases from January 1990 until December 2016 generated close to 4400 articles, of which only 26 met the inclusion criteria. Evidence indicate that affective expressions were mostly examined through laboratory and naturalistic observations, within a wide range of interactive contexts in which mother–child dyads significantly outnumbered father–child dyads. Moreover, dyadic partners were found to match each others’ displays and positive and neutral facial expressions proving more frequent than negative facial expressions. Finally, researchers observed some developmental and gender differences regarding the frequency, intensity, and category of emotional displays and identified certain links among facial expression behavior, family relations, personal adjustment, and peer-related social competence.     

Graphical Techniques for Selecting Explanatory Variables for Time Series Data

Bayesian model building techniques are developed for data with a strong time series structure and possibly exogenous explanatory variables that have strong explanatory and predictive power. The emphasis is on finding whether there are any explanatory variables that might be used for modelling if the data have a strong time series structure that should also be included. We use a time series model that is linear in past observations and that can capture both stochastic and deterministic trend, seasonality and serial correlation. We propose the plotting of absolute predictive error against predictive standard deviation. A series of such plots is utilized to determine which of several nested and non-nested models is optimal in terms of minimizing the dispersion of the predictive distribution and restricting predictive outliers. We apply the techniques to modelling monthly counts of fatal road crashes in Australia where economic, consumption and weather variables are available and we find that three such variables should be included in addition to the time series filter. The approach leads to graphical techniques to determine strengths of relationships between the dependent variable and covariates and to detect model inadequacy as well as determining useful numerical summaries.     

The graphical advantages of finite interval confidence band procedures

When presented as graphical illustrations, regression surface confidence bands for linear statistical models quickly convey detailed information about analysis results. A taut confidence band is a compact set of curves which are estimation candidates for the unobservable, fixed regression curve. The bounds of the band are usually plotted with the estimated regression curve and may be overlaid by a scatter-plot of the data to provide an integrated visual impression. Finite-interval confidence bands offer the advantages of clearer interpretation and improved efficiency and avoid visual ambiguities inherent to infinite-interval bands. The definitive characteristic of a finite-interval confidence band is that it is only necessary to plot it over a finite interval in order to visually communicate all its information. In contrast, visual representations of infinite-interval bands are not fully informative and can be misleading. When an infinite-interval band is plotted, and therefore truncated, substantial information given by its asymptotic behavior is lost. Many curves that are clearly within the plotted portion of the infinite interval confidence band eventually cross a boundary. In practice, a finite-interval band can always be easily obtained from any infinite-interval band. This article focuses on interpretational considerations of symmetric confidence bands as graphical devices.     

Popular and Rejected Children's Reasoning Regarding Negative Emotions in Social Situations: The Role of Gender

Two studies compared popular and rejected children's reasoning regarding social interactions involving negative emotions. The first study, with 23 rejected and 23 popular 10‐ to 11‐year‐olds, involved hypothetical social scenarios where a classmate ‘victim’ was likely to experience a negative emotion. Although popular and rejected children both recognized negative emotions and were equally likely to suggest helping behaviour to aid the victim, there were gender effects on the type of helping behaviour suggested. Specifically, popular girls were significantly more likely to offer comforting behaviour than advice whereas popular boys offered advice more than comfort; no such preferences were exhibited by the rejected children. Furthermore, popular girls were significantly more likely than other children to refer to emotional states when justifying their helping response. In the second study, 30 popular and 30 rejected eight‐ to 10‐year‐olds identified the motives behind story characters' efforts to mask negative emotions. Popular girls were more likely to identify the target motives than rejected girls, but no such difference was apparent for the boys. The results are discussed in the light of evidence regarding gender differences in peer interaction patterns.     

Efficient estimation and model selection in large graphical models

We develop a computationally efficient method to determine the interaction structure in a multidimensional binary sample. We use an interaction model based on orthogonal functions, and give a result on independence properties in this model. Using this result we develop an efficient approximation algorithm for estimating the parameters in a given undirected model. To find the best model, we use a heuristic search algorithm in which the structure is determined incrementally. We also give an algorithm for reconstructing the causal directions, if such exist. We demonstrate that together these algorithms are capable of discovering almost all of the true structure for a problem with 121 variables, including many of the directions.     

Mixed Graphical Model Selection Using Holm's Procedure

The problem of selecting a graphical model is considered as a performing simultaneously multiple tests. The control of the overall Type I error on the selected graph is done using the so famous Holm's procedure. We prove that when we use a consistent edge exclusion test the selected graph is asymptotically equal to the true graph with probability at least equal to a fixed level 1 ? α. This method is then used for the selection of mixed concentration graph models by performing the χ²-edge exclusion test. We also apply the method to two classical examples and to simulated data. We compare the overall error of the selected model with the one obtained using the stepwise method. We establish that the control is better when we use the Holm's procedure.     

Fisher's Hypergeometric Test for a Comparison in a Finite Population

Graphical methods have played a central role in the development of statistical theory and practice. This presentation briefly reviews some of the highlights in the historical development of statistical graphics and gives a simple taxonomy that can be used to characterize the current use of graphical methods. This taxonomy is used to describe the evolution of the use of graphics in some major statistical and related scientific journals.

Some recent advances in the use of graphical methods for statistical analysis are reviewed, and several graphical methods for the statistical presentation of data are illustrated, including the use of multicolor maps.