The use of small scale prototypes in image reconstruction from projections

We introduce a class of small scale simulation models—‘prototypes'—which reproduce many of the known properties of maximum likelihood and related reconstruction methods used in emission tomography, and greatly simplify the development of new methods. We introduce an iterative Fisher-scoring algorithm and demonstrate, by use of the prototype models, its superior speed of convergence when compared with the standard EM algorithm.     

Nonparametric functionals of spectral distributions and their applications to time series analysis

There is a close analogy between empirical distributions of i.i.d. random variables and normalized spectral distributions of wide-sense stationary processes. Herein we make use of this analogy to develop nonparametric comparisons of two spectral distributions and nonparametric tests of stationarity versus change-point alternatives via spectral analysis of a time series.     

Statistical inference in dynamic panel data models

Anderson and his collaborators have made seminal contributions to inference with instrumental variables and to dynamic panel data models. We review these contributions and the extensive economic and statistical literature that these contributions spawned. We describe our recent work in these two areas, presenting new approaches to (a) making valid inferences in the presence of weak instruments and (b) instrument and model selection for dynamic panel data models. Both approaches use empirical likelihood and resampling. For inference in the presence of weak instruments, our approach uses model averaging to achieve asymptotic efficiency with strong instruments but maintain valid inferences with weak instruments. For instrument and model selection, our approach aims at choosing valid instruments that are strong enough to be useful.     

Making feminism count: integrating feminist research principles in large-scale quantitative research on violence against women and girls

ABSTRACT

Applying a feminist approach to research on ending violence against women and girls (VAWG) is critical because gender inequality is an underlying driver of VAWG, and feminist research aims to empower women and girls, as well as challenge prevailing inequalities through the research process itself. However, feminist research approaches have not historically been applied in the international development sector, although statistical evidence on what works to end VAWG is in high demand from governments and donors. In this article, we explore how researchers could practically reconcile an explicitly feminist undertaking, like ending VAWG, when accepted research practices within this field employ methods that are historically not informed by feminist praxis. We argue that quantitative research and feminist research approaches are not mutually exclusive, rather, they can (and do) overlap. Drawing on five decades of combined experience conducting quantitative studies on VAWG in low- and middle-income countries around the world, we highlight the challenges and opportunities for incorporating feminist research principles throughout the research process – from design, community engagement, data collection, analysis, dissemination, and policy influence. We draw on practical examples from research conducted in countries as diverse as Timor-Leste, Kiribati, and Sri Lanka, among others, illustrating that it is not only possible to apply feminist research principles to large-scale, quantitative survey research on VAWG, but that this should become a priority for good development practice.     

Multi-site aggregate production planning with multiple objectives: A goal programming approach

This paper addresses the problem of aggregate production planning (APP) for a multinational lingerie company in Hong Kong. The multi-site production planning problem considers the production loading plans among manufacturing factories subject to certain restrictions, such as production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers' preferences, as well as production capacity, workforce level, storage space and resource conditions of the factories. In this paper, a multi-objective model is developed to solve the production planning problems, in which the profit is maximized but production penalties resulting from going over/under quotas and the change in workforce level are minimized. To enhance the practical implications of the proposed model, different managerial production loading plans are evaluated according to changes in future policy and situation. Numerical results demonstrate the robustness and effectiveness of the developed model.     

Factor analyzing the “ASK” cultural competency self-assessment scale for child protective services

This study explored the development of a short version of “ASK” (Attitude–Skills–Knowledge) Scale — a tool for caseworkers and supervisors self-assessing cultural competency in a training context. The goal was to determine the validity and reliability of the 97-item long form and find factors to consolidate the items to create a shorter tool for training and practice use. In a state project, 171 caseworkers and supervisors rated the level of importance of 97 areas of attitudes, skills, and knowledge competency in terms of self-assessing their readiness to work in a culturally diverse environment. Attitude, skill, and knowledge domains of the instrument were tested with high reliability (.93 for the 30 “attitude” items, .95 for the 40 “skill” items, and .92 for the 27 “knowledge” items). Factor analyses indicate that this scale can be efficiently applied to categorize the 30 attitude items into eight factors, the 40 skill items into nine factors, and the 27 knowledge items into seven factors. With these 24 factors, a short ASK version was constructed for future research to encourage continuous self-assessment.     

Varicella immunization requirements for US colleges: 2014–2015 academic year

ABSTRACT

Objective: To obtain information on varicella prematriculation requirements in US colleges for undergraduate students during the 2014–2015 academic year. Participants: Health care professionals and member schools of the American College Health Association (ACHA). Methods: An electronic survey was sent to ACHA members regarding school characteristics and whether schools had policies in place requiring that students show proof of 2 doses of varicella vaccination for school attendance. Results: Only 27% (101/370) of schools had a varicella prematriculation requirement for undergraduate students. Only 68% of schools always enforced this requirement. Private schools, 4-year schools, northeastern schools, those with <5,000 students, and schools located in a state with a 2-dose varicella vaccine mandate were significantly more likely to have a varicella prematriculation requirement. Conclusions: A small proportion of US colleges have a varicella prematriculation requirement for varicella immunity. College vaccination requirements are an important tool for controlling varicella in these settings.     

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.