The Mother‐Citizen and the Working Girl: First‐Wave Feminist Citizenship Claims in Canada and Discursive Opportunities for Twenty‐First Century Childcare Policy

L'État providence Canadien est déséquilibré: leader mondial dans certains domaines de l'investissement social—plus particulièrement en soins de santé et enseignement supérieur—il traîne dans d'autres domaines de la dépense d'aide sociale, y compris les politiques de garde d'enfants. Nous constatons qu'aujourd'hui la résistance dans la plupart des régions du Canada à la politique de garde d'enfants universel a des racines historiques profondes. Elle est de fait intégré dans le cadre même du bien‐être social canadien: le contrat de citoyenneté sociale. Nous employons une approche de “bas en haut” sur l'état du bien‐être en nous concentrant sur la façon dont les acteurs collectifs, en particulier, les féministes de la première vague et leurs contemporains au sein des organisations du mouvement ouvrier, ont encadrées et caractérisé la citoyenneté sociale féminine; Nous constatons que l'exclusion de l'adhésion des femmes employées tronque les possibilités discursives par lesquelles des revendications pour des provisions universelles de bien‐être sociale pourraient être apportées. Canada's welfare state is lopsided: while leading the world in some areas of social investment—most notably health care and higher education—it is a laggard in other areas of social welfare spending, including childcare policies. We find that policy resistance to universal childcare in most regions of Canada today has deep historical roots and is, indeed, embedded into the very framework of the Canadian welfare state: the social citizenship contract. We employ a “bottom‐up” perspective on the welfare state focusing on how collective actors, in particular, first‐wave feminists and their contemporaries within labor movement organizations, framed and characterized female social citizenship in ways that excluded employed women from full membership and, in this, truncated discursive opportunities by which claims for universal welfare state provisions could be made.     

Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications

In modeling disease transmission, contacts are assumed to have different infection rates. A proper simulation must model the heterogeneity in the transmission rates. In this article, we present a computationally efficient algorithm that can be applied to a population with heterogeneous transmission rates. We conducted a simulation study to show that the algorithm is more efficient than other algorithms for sampling the disease transmission in a subset of the heterogeneous population. We use a valid stochastic model of pandemic influenza to illustrate the algorithm and to estimate the overall infection attack rates of influenza A (H1N1) in a Canadian city.     

A variable selection approach to monotonic regression with Bernstein polynomials

One of the standard problems in statistics consists of determining the relationship between a response variable and a single predictor variable through a regression function. Background scientific knowledge is often available that suggests that the regression function should have a certain shape (e.g. monotonically increasing or concave) but not necessarily a specific parametric form. Bernstein polynomials have been used to impose certain shape restrictions on regression functions. The Bernstein polynomials are known to provide a smooth estimate over equidistant knots. Bernstein polynomials are used in this paper due to their ease of implementation, continuous differentiability, and theoretical properties. In this work, we demonstrate a connection between the monotonic regression problem and the variable selection problem in the linear model. We develop a Bayesian procedure for fitting the monotonic regression model by adapting currently available variable selection procedures. We demonstrate the effectiveness of our method through simulations and the analysis of real data.     

Exact confidence bounds for an exponential parameter under hybrid censoring

Consider a life testing experiment in which n units are put on test, successive failure times are recorded, and the observation is terminated either at a specified number r of failures or a specified time T whichever is reached first. This mixture of type I and type II censoring schemes, called hybrid censoring, is of wide use. Under this censoring scheme and the assumption of an exponential life distribution, the distribution of the maximum likelihood estimator of the mean life θ is derived. It is then used to construct an exact lower confidence bound for θ.     

Confounding in mixed factorial experiments through isomorphisms

The derivation of a simpie mexhoa for confounding in mixed factorial experiments from an isomorphism of finite abelian groups is presented. The theoretical bases of confounding procedures that use modular arithmetic for such experiments are compared.     

Exact confidence bounds for an exponential parameter under hybrid censoring

Consider a life testing experiment in which n units are put on test, successive failure times are recorded, and the observation is terminated either at a specified number r of failures or a specified time T whichever is reached first. This mixture of type I and type II censoring schemes, called hybrid censoring, is of wide use. Under this censoring scheme and the assumption of an exponential life distribution, the distribution of the maximum likelihood estimator of the mean life 6 is derived. It is then used to construct an exact lower confidence bound for θ.     

The present status of confidence interval estimation on variance components in balanced and unbalanced random models

Much research has been conducted to develop confidence Intervals on linear combinations and ratios of variance components in balanced and unbalanced random models.This paper first presents confidence intervals on functions of variance components in balanced designs.These results assume that classical analysis of variance sums of squares are independent and have exact scaled chi-squared distributions.In unbalanced designs, either one or both of these assumptions are violated, and modifications to the balanced model intervals are required.We report results of some recent work that examines various modifications for some particular unbalanced designs.     

Bayesian outlier testing using the predictive distribution for a linear model op constant intraclass form

The problem of testing suspected outliers from a linear model with constant intraclass correlation is considered from a Bayesian viewpoint. The main objective of this paper is to develop an outlier test procedure based on the predictive distribution of suspected outlier observations given a set of existing inlier observations. The test procedure is easily performed with the usual F and t distributions.     

A simpler way of obtaining non-sIngularity conditions of rotatability

In response surface designs, it is not usually easy to handle the moment matrix X'X, especially for higher orders. This paper presents a method in which the moment matrix of a response surface of any order can be standardized, i.e., X'X splits into a diagonal matrix consisting of sub-matrices of lower order. This eases the calculation of the determinant and the inverse of X'X. The method has been illustrated with applications to second, third and fourth order response surfaces.     

Exact moments of the symmetric cubic assignment statistic

Hubert (1987Assignment Methods in Combinatorial Data Analysis) presented a class of permutation, or random assignment, techniques for assessing correspondence between general k-dimensional proximity measures on a set of “objects.” A major problem in higher-order assignment models is the prohibitive level of computation that is required. We present the first three exact moments of a test statistic for the symmetric cubic assignment model. Efficient computational formulas for the first three moments have been derived, thereby permitting approximation of the permutation distribution using well-known methods.