Families,Not Parents,Differ: Development of Communication in Finnish Infants

This longitudinal study on Finnish families was conducted to identify developmental differences in family‐level communication among mothers, fathers, and their infants during the second half of the infant's first year, and associations with infants' later language and communicative skills. We examined coregulated communication of parent‐infant dyads during 5‐min laboratory play sessions at 7 and 11 months. Few differences in mutually regulated communicative exchanges emerged between maternal and paternal dyads, and few developmental changes were found across the whole sample. Families with different communication profiles were identified, and changes rather than stability characterized communicative development at the family level. The family‐level differences at 7 months predicted variation in children's language and communicative skills at 14 months.     

Optimal Method for Realizing Two-Sided Inferences About a Linear Combination of Two Proportions

In order to reach the inference about a linear combination of two independent binomial proportions, various procedures exist (Wald's classic method, the exact, approximate, or maximized score methods, and the Newcombe-Zou method). This article defines and evaluates 25 different methods of inference, and selects the ones with the best behavior. In general terms, the optimal method is the classic Wald method applied to the data to which z ² _α/2/4 successes and z ² _α/2/4 failures are added (≈1 if α = 5%) if no sample proportion has a value of 0 or 1 (otherwise the added increase may be different).

Supplemental materials are available for this article. Go to the publisher's online edition of Communications in Statistics - Simulation and Computation to view the free supplemental file.     

A procedure to select a ML-II prior in a multivariate normal case

Berger (1985) derived a procedure to select a maximum likelihood II prior distribution. In this paper a method is suggested to construct such a prior distribution from a multivariate ε-contamination class of distributions. The method is illustrated by the conetruction of a ML-II prior in the multivariate normal case.     

Risk Perception Among Smokers: A Qualitative Study

The aim of this qualitative study is to identify the dimensions people used to assess the risk of smoking and then compare them with those used by health professionals in primary healthcare. Five discussion groups were conducted. The field work was carried out in Barcelona (Spain), from February 2005 to January 2006. Data were analyzed following a semantic‐thematic categorical content analysis (ACC‐ts). Results showed that people tend to employ stereotypical discourses when evaluating the risk of smoking. Similarly, they reassess the risk in the context of their life experience and incorporate new nuances to the arguments sustaining their behavior. Once this reassessment takes place, the decision to continue smoking emerges, and smokers come up with additional arguments justifying their habit (i.e., age, benefits related to costs). Professionals are aware of this process and its multidimensional nature. Nevertheless, their discourse loses this multidimensional feature when discussing the strategies they use at smoking cessation interventions. This qualitative study increases the understanding of various meanings that people attribute to their life experience. These assumptions may be useful for developing health practices that are closer to people. As a practical utility of these results, it would be interesting to apply a preliminary assessment of the different meanings that people attribute to smoking from their life context in risk communication.     

Why is not design of experiments widely used by engineers in Europe?

An extensive literature review was carried out to detect why design of experiments (DoE) is not widely used among engineers in Europe. Once 16 main barriers were identified, a survey was carried out to obtain first-hand information about the significance of each. We obtained 101 responses from academics, consultants and practitioners interested in DoE. A statistical analysis of the survey is introduced, including: (a) a ranking of the barriers, (b) grouping of barriers using factorial analysis, (c) differences between characteristics of respondents. This exploratory analysis showed that the main barriers that hinder the widespread use of DoE are low managerial commitment and engineers’ general weakness in statistics. Once the barriers were classified, the most important resultant group was that related to business barriers.     

Non-parametric detection of meaningless distances in high dimensional data

Distance concentration is the phenomenon that, in certain conditions, the contrast between the nearest and the farthest neighbouring points vanishes as the data dimensionality increases. It affects high dimensional data processing, analysis, retrieval, and indexing, which all rely on some notion of distance or dissimilarity. Previous work has characterised this phenomenon in the limit of infinite dimensions. However, real data is finite dimensional, and hence the infinite-dimensional characterisation is insufficient. Here we quantify the phenomenon more precisely, for the possibly high but finite dimensional case in a distribution-free manner, by bounding the tails of the probability that distances become meaningless. As an application, we show how this can be used to assess the concentration of a given distance function in some unknown data distribution solely on the basis of an available data sample from it. This can be used to test and detect problematic cases more rigorously than it is currently possible, and we demonstrate the working of this approach on both synthetic data and ten real-world data sets from different domains.     

A necessary and sufficient condition for an estimator to be optimal

A necessary condition for an estimator δ₀ to be optimal in a class Δ of estimators is derived. Thic condition becomes also sufficient when the loss function is convex.     

Analysis of bivariate dichotomous data from a stratified two-stage cluster sample

In the health and social sciences, researchers often encounter categorical data for which complexities come from a nested hierarchy and/or cross-classification for the sampling structure. A common feature of these studies is a non-standard data structure with repeated measurements which may have some degree of clustering. In this paper, methodology is presented for the joint estimation of quantities of interest in the context of a stratified two-stage sample with bivariate dichotomous data. These quantities are the mean value π of an observed dichotomous response for a certain condition or time-point and a set of correlation coefficients for intra-cluster association for each condition or time period and for inter-condition correlation within and among clusters. The methodology uses the cluster means and pairwise joint probability parameters from each cluster. They together provide appropriate information across clusters for the estimation of the correlation coefficients.