The development of social statistics and indicators on the status of women

In developing social statistics and indicators on the status of women, three requirements must be satisfied. First, existing measures of prevalent demographic, socio-economic, and socio-cultural conditions must be more fully utilized. Second, some improvements in these measures, together with certain new measures and data sets are needed. Finally, agreed standards of comparison for gauging the relative status of women must be established. In each of the above subject-areas, some widely available statistics and derived measures are examined with respect to their adequacy for describing women's living conditions and assessing their status. Proposals are then offered for additional measures and new data collection efforts designed to reduce the deficiencies or gaps in existing data and provide a sounder basis for assessing the status of women in different societies.     

Infant‐directed speech to infants at risk for dyslexia: A novel cross‐dyad design

When mothers speak to infants at risk for developmental dyslexia, they do not hyperarticulate vowels in their infant‐directed speech (IDS). Here, we used an innovative cross‐dyad design to investigate whether the absence of vowel hyperarticulation in IDS to at‐risk infants is a product of maternal infant‐directed behavior or of infants’ parent‐directed cues. Interactions between mothers and infants who were at risk or not at risk for dyslexia were recorded in three conditions: when mothers interacted with (a) their own infants, (b) infants who were not their own but of the same risk status, and (c) infants who were not their own and of the opposite risk status. This design revealed both infant and parent effects. Mothers of not‐at‐risk infants hyperarticulated vowels significantly more when speaking to not‐at‐risk than to at‐risk infants. In contrast, mothers of at‐risk infants hyperarticulated vowels significantly less than NAR mothers, and this was irrespective of infant status. Mothers of not‐at‐risk infants thus adjusted their IDS to the infant's risk status, while mothers of at‐risk infants did not. We suggest that IDS is determined reciprocally by characteristics of both partners in the dyad: Both infant and maternal factors are essential for the vowel hyperarticulation component of IDS.     

Analysing survey data with incomplete responses by using a method based on empirical likelihood

Summary.  In many surveys, missing response is a common problem. As an example, Zahner, Jacobs, Freeman and Trainor analysed data from a study of child psychopathology in the State of Connecticut, USA. In that study, the response variable, psychopathology, was inferred from questions that were addressed to teachers of the children and was subject to a high level of missingness. However, the missing responses were supplemented by surrogate information that was provided by the parents and/or the primary care providers of the children. In such a situation, it is conceivable that the supplemental information can be used to recover some of the information that has been lost in the cases with missing response. This paper considers a method using empirical likelihood. Empirical likelihood is well known in providing nonparametric inference. But its application has largely been confined to complete-data situations. The method proposed exploits the semiparametric nature of empirical likelihood. The method gives consistent estimates if the cases with non-missing responses form a random sample of the population. In large samples, the method behaves similarly to a regression estimate that is applied to estimating equations. The method is easy to implement with standard statistical packages. In a small sample study, the method was found to give favourable results, when compared with existing methods.     

Estimation suroptimale de la densité par projection

Superefficiency of a projection density estimator The author constructs a projection density estimator with a data‐driven truncation index. This estimator reaches the superoptimal rates 1/n in mean integrated square error and {In ln(n/n}^1/2 in uniform almost sure convergence over a given subspace which is dense in the class of all possible densities; the rate of the estimator is quasi‐optimal everywhere else. The subspace in question may be chosen a priori by the statistician.