User-centred responses to child sexual abuse: the way forward?

Two parallel surveys were carried out based in the north-east of England and Cumbria: one ascertaining statutory social work services provided for child and adult survivors of sexual abuse, the other drawing upon the perceptions of adult survivors of child sexual abuse about existing and desirable forms of provision for adults and children. The surveys indicate the need for greater genuine partnership between service users and statutory agencies in the field of child sexual abuse, similar to that which is being pioneered in the field of adult care and disability. A community-orientated response to sexual abuse, centred on service-users, is required if one of the largest social problems facing us today is to be effectively challenged.     

Mutual benefit societies in Hungary, 1830-1941

In Hungary for most of the nineteenth century mutual benefit societies played a key role in providing various social groups with aid in case of illness, accident, disability or death. The majority were linked to the burgeoning working-class movement and were voluntary associations whose members had no other recourse in times of adversity. Hungary was undergoing transformation in the nineteenth century. Changes in goods production and the craft guilds accelerated the establishment of workers'benefit societies, and so did the lack of a coherent system of social insurance in the new industrial order. They grew in tandem with the expansion of Hungary's economy and the return of constitutional government in 1867, and flourished from the 1870s to the decade preceding the First World War. Austria and Germany provided theoretical justifications and practical models. By the 1890s voluntary mutual benefit societies faced increasing competition from the State. As the economy and social structures evolved, successive governments sponsored their own insurance programmes. By the turn of the century, private companies were also offering life and accident insurance and the appeal of mutual benefit societies to both workers and the middle class diminished. Between the two world wars, they continued to serve a substantial segment of the population.     

Practical Methods for Bounding Type I Error Rate with an Internal Pilot Design

New analytic forms for distributions at the heart of internal pilot theory solve many problems inherent to current techniques for linear models with Gaussian errors. Internal pilot designs use a fraction of the data to re-estimate the error variance and modify the final sample size. Too small or too large a sample size caused by an incorrect planning variance can be avoided. However, the usual hypothesis test may need adjustment to control the Type I error rate. A bounding test achieves control of Type I error rate while providing most of the advantages of the unadjusted test. Unfortunately, the presence of both a doubly truncated and an untruncated chi-square random variable complicates the theory and computations. An expression for the density of the sum of the two chi-squares gives a simple form for the test statistic density. Examples illustrate that the new results make the bounding test practical by providing very stable, convergent, and much more accurate computations. Furthermore, the new computational methods are effectively never slower and usually much faster. All results apply to any univariate linear model with fixed predictors and Gaussian errors, with the t-test a special case.     

An alternative to unit root tests: Bridge estimators differentiate between nonstationary versus stationary models and select optimal lag

This paper introduces a novel way of differentiating a unit root from stationary alternatives using so-called “Bridge” estimators; this estimation procedure can potentially generate exact zero estimates of parameters. We exploit this property and treat this as a model selection problem. We show that Bridge estimators can select the correct model with probability tending to 1. They estimate “zero” parameter on the lagged dependent variable as zero (nonstationarity), if this is nonzero (stationary), estimate the coefficient with standard normal limit. In this sense, we extend the statistics literature as well, since that literature only deals with model selection among only stationary variables.     

State estimation for nonlinear state-space transmission models of tuberculosis

Given the high prevalence of tuberculosis (TB) and the mortality rate associated with the disease, numerous models, such as the Gammaitoni and Nucci (GN) model, were developed to model the risk of transmission. These models typically rely on a quanta generation rate as a measurement of infectivity. Since the quanta generation rate cannot be measured directly, the unique contribution of this work is to develop state estimators to estimate the quanta generation rate from available measurements. To estimate the quanta generation rate, the GN model is adapted into an augmented single-room GN model and a simplified two-room GN model. Both models are shown to be observable, i.e., it is theoretically possible to estimate the quanta generation rate given available measurements. Kalman filters are used to estimate the quanta generation rate. First, a continuous-time extended Kalman filter is used for both adapted models using a simulation and measurement sampling rate of 60 s. Accurate quanta generate rate estimates are achieved in both cases. A more realistic scenario is also considered with a measurement sampling rate of one day. For these estimates, a hybrid extended Kalman filter (HEKF) is used. Accurate quanta generation rate estimates are achieved for the more realistic scenario. Future work could potentially use the HEKFs, the adapted models, and real-time measurements in a control system feedback loop to reduce the transmission of TB in confined spaces such as hospitals.     

MODELING DEMAND DURING LEAD TIME*

This paper treats some of the important considerations in constructing an analytical model for the distribution of demand during lead time. It presents a formal model that can be developed along one of two lines. The first has order size and order intensity leading to a compound distribution of period demand, then period demand and lead time giving rise to a compound distribution of demand during lead time. The second has order intensity and lead time giving rise to a compound distribution of lead-time order intensity, then lead-time order intensity and order size leading to a compound distribution of demand during lead time. The paper also condenses the state of the art in a table and proposes some simple classification schemes that could help researchers extend that state of the art.