In this article, the employment characteristics of pre-industrial and industrial cohorts of deaf men and women are compared with each other, as well as with a cohort of non-disabled siblings. The aim is to determine the extent to which the employment patterns of deaf persons lined up with those of non-disabled people and to see how nineteenth-century industrialization processes influenced their employment opportunities. This article challenges the widely held assumption that the nineteenth century constituted a definitive break by arguing that the professional lives of deaf people were not necessarily better before industrialization. Moreover, I demonstrate that the development of deaf schools in the course of the nineteenth century opened a new range of career opportunities for deaf individuals. 相似文献
For large cohort studies with rare outcomes, the nested case-control design only requires data collection of small subsets of the individuals at risk. These are typically randomly sampled at the observed event times and a weighted, stratified analysis takes over the role of the full cohort analysis. Motivated by observational studies on the impact of hospital-acquired infection on hospital stay outcome, we are interested in situations, where not necessarily the outcome is rare, but time-dependent exposure such as the occurrence of an adverse event or disease progression is. Using the counting process formulation of general nested case-control designs, we propose three sampling schemes where not all commonly observed outcomes need to be included in the analysis. Rather, inclusion probabilities may be time-dependent and may even depend on the past sampling and exposure history. A bootstrap analysis of a full cohort data set from hospital epidemiology allows us to investigate the practical utility of the proposed sampling schemes in comparison to a full cohort analysis and a too simple application of the nested case-control design, if the outcome is not rare.
It is well-known that, under Type II double censoring, the maximum likelihood (ML) estimators of the location and scale parameters, θ and δ, of a twoparameter exponential distribution are linear functions
of the order statistics. In contrast, when θ is known, theML estimator of δ does not admit a closed form expression. It is shown, however, that theML estimator of the scale parameter exists and is unique. Moreover, it has good large-sample properties. In addition, sharp
lower and upper bounds for this estimator are provided, which can serve as starting points for iterative interpolation methods
such as regula falsi. Explicit expressions for the expected Fisher information and Cramér-Rao lower bound are also derived.
In the Bayesian context, assuming an inverted gamma prior on δ, the uniqueness, boundedness and asymptotics of the highest
posterior density estimator of δ can be deduced in a similar way. Finally, an illustrative example is included. 相似文献