Rich Sensitivities: An Analysis of Conflict Among Women in Feminist Memoir*

Alors que le mouvement nord‐américain des femmes est surtout connu pour ses efforts en vue de transformer les relations sociales entre les femmes et les hommes, ses partisanes ont également mis l'accent sur la reformulation des relations entre les femmes. Utilisant une source de données novatrice, les mémoires du mouvement social, l'auteure souligne la profondeur de la déception que les féministes s'infligent les unes aux autres. Les mémorialistes débattent des notions provenant du mouvement et du courant principal voulant que les femmes soient compétentes socialement. Cet article présente le concept d'«idéation relationnelle» pour décrire la façon dont les mémorialistes féministes analysent d'une manière critique la compréhension de la socialité des femmes tenue pour acquise et aiguisent leurs désirs d'une nouvelle éthique sociale entre elles. While the North American women's movement is most known for its efforts to transform social relations between women and men, its adherents have also focused on remaking relations among women. Using an innovative data source, social movement memoir, this paper indicates the depth of disappointment feminists cause one another. Memoirists dispute notions found in the movement and mainstream that women are socially capable. The paper offers the concept “relational ideation” to describe the way feminist memoirists critically examine taken for granted understandings of women's sociality and amplify their desires for a new social ethic among them.     

Structural Nested Models and Standard Software: A Mathematical Foundation through Partial Likelihood

Abstract.  In observational studies treatment may be adapted to the patient's state during the course of time. These covariates may in turn also react on the treatment under study, and so on. This makes it hard to distinguish between treatment effect and selection bias. Structural nested models aim at estimating treatment effect in such complicated situations, even when treatment may change at any time. We show that structural nested models can often be calculated with standard software, by using standard models to predict treatment as a tool to estimate treatment effect. Robins ( Survival analysis, Volume 6 of Encyclopedia of Biostatistics , John Wiley and Sons, Chichester, 1998) conjectured this, but so far it was unproven. We use a partial likelihood approach to choose the estimators and tests as a subclass of the estimators and tests in Lok (math. ST/0410271 at http://arXiv.org , 2004). We show that this is the class of estimators and tests that can be calculated with standard software. The estimators are consistent and asymptotically normal, and have interesting asymptotic properties.     

DIRTY OLD WOMEN: BUCK BROWN'S CARTOONS*

Une portion importante des écrits contemporains féministes étudie des mythes au sujet de la femme. L'analyse des mythes oraux classiques et l'analyse des mythes littéraires et des images de la femme dans la presse, la radio, et la télévision contribuent à une compréhension de l'élaboration des stéréotypes masculin/féminin dans notre société. L'article analyse une série de caricatures portant sur une ‘dirty old woman’ qui ont paru dans la revue Playboy. Myths about women are examined in an important segment of contemporary feminist writings. Analyses of classical oral myths, of literary myths, and of images of women in the press, radio, and television contribute to our understanding of the construction of male/female stereotypes in our society. This paper analyses a series of cartoons in Playboy magazine which focus on a ‘dirty old woman.’     

Bayesian Goodness of Fit Testing with Mixtures of Triangular Distributions

Abstract.  We consider the consistency of the Bayes factor in goodness of fit testing for a parametric family of densities against a non-parametric alternative. Sufficient conditions for consistency of the Bayes factor are determined and demonstrated with priors using certain mixtures of triangular densities.     

The early history of Scottish child welfare

SUMMARY. There is a growing interest in the history of childhood and child care. This article brings together from disparate sources some of the details and themes concerning the emergence and development of public and voluntary child welfare in Scotland. Particular attention is given to residential and foster care arrangements under the Poor Law and to the influence of ‘philanthropic’ individuals and organisations. Comparative references are made to the contemporary situation in England and Wales and to present day practice.     

Bayesian Optimal Adaptive Estimation Using a Sieve Prior

We derive rates of contraction of posterior distributions on non‐parametric models resulting from sieve priors. The aim of the study was to provide general conditions to get posterior rates when the parameter space has a general structure, and rate adaptation when the parameter is, for example, a Sobolev class. The conditions employed, although standard in the literature, are combined in a different way. The results are applied to density, regression, nonlinear autoregression and Gaussian white noise models. In the latter we have also considered a loss function which is different from the usual l ² norm, namely the pointwise loss. In this case it is possible to prove that the adaptive Bayesian approach for the l ² loss is strongly suboptimal and we provide a lower bound on the rate.     

Rates of Convergence for a Bayesian Level Set Estimation

Abstract.  We are interested in estimating level sets using a Bayesian non-parametric approach, from an independent and identically distributed sample drawn from an unknown distribution. Under fairly general conditions on the prior, we provide an upper bound on the rate of convergence of the Bayesian level set estimate, via the rate at which the posterior distribution concentrates around the true level set. We then consider, as an application, the log-spline prior in the two-dimensional unit cube. Assuming that the true distribution belongs to a class of Hölder, we provide an upper bound on the rate of convergence of the Bayesian level set estimates. We compare our results with existing rates of convergence in the frequentist non-parametric literature: the Bayesian level set estimator proves to be competitive and is also easy to compute, which is of no small importance. A simulation study is given as an illustration.