Numerical solution of optimal allocation problems in stratified sampling under box constraints

Modern sampling designs in survey statistics, in general, are constructed in order to optimize the accuracy of estimators such as totals, means and proportions. In stratified random sampling a variance minimal solution was introduced by Neyman and Tschuprov. However, practical constraints may lead to limitations of the domain of sampling fractions which have to be considered within the optimization process. Special attention on the complexity of numerical solutions has to be paid in cases with many strata or when the optimal allocation has to be applied repeatedly, such as in iterative solutions of stratification problems. The present article gives an overview of recent numerical algorithms which allow adequate inclusion of box constraints in the numerical optimization process. These box constraints may play an important role in statistical modeling. Furthermore, a new approach through a fixed point iteration with a finite termination property is presented.     

‘Zealotry or Nostalgic Regret’?1 Women Leaders in Technical and Further Education in Australia: Agents of Change,Entrepreneurial Educators or Corporate Citizens?

Education has been restructured in many Western post‐industrial nation states during the 1990s. The Australian Technical and Further Education sector (TAFE) has been particularly susceptible to discourses of responsiveness to the market and the new entrepreneuralism. This article explores how women have been repositioned in contradictory and ambiguous ways as the new entrepreneurial middle managers by existing and emergent discourses that circulated in and through TAFE organizations. In turn, it points to how discourses of change management and client responsiveness took on particular readings within specific institutional and professional cultures of the eight Technical and Further Education institutions (TAFEs). At the same time, the restructuring that arose from the corporatization of TAFE, in a highly gendered process, through the twin strategies of marketization and the new managerialism produced new possibilities for individual women educators who moved up into middle management. Yet these individual women were positioned within highly masculinist ‘neo‐corporate bureaucratic cultures’ that co‐opted their passion for the capacity of education to make a difference and incorporated these new entrepeneurial work identities.     

Going Public: Networking Globally and Locally*

Abstract Rural sociologists figure prominently in the move towards public sociology. The paper takes up Michael Burawoy's call for public sociology and discusses what rural sociologists have to offer to publics and how we stand to gain as a discipline in working with publics. The paper argues that rural sociologists' ability to adopt a cosmopolitan view while negotiating the complexities of global/local processes provides a useful theoretical stance for doing public sociology. Methodologically, both feminist methods and various approaches to networks can guide us as we do public sociology. Then, the paper provides two examples of recent efforts to do public sociology with a women's community group in Sri Lanka in response to the tsunami and with the Pennsylvania Women's Agricultural Network to illustrate the possibilities and limitations of working with networks. In conclusion, the paper addresses opportunities for doing public sociology, the challenges we face as we go public, and future work that is needed to develop theoretically and methodologically strong public rural sociology.     

Block‐threshold‐adapted Estimators via a Maxiset Approach

We study the maxiset performance of a large collection of block thresholding wavelet estimators, namely the horizontal block thresholding family. We provide sufficient conditions on the choices of rates and threshold values to ensure that the involved adaptive estimators obtain large maxisets. Moreover, we prove that any estimator of such a family reconstructs the Besov balls with a near‐minimax optimal rate that can be faster than the one of any separable thresholding estimator. Then, we identify, in particular cases, the best estimator of such a family, that is, the one associated with the largest maxiset. As a particularity of this paper, we propose a refined approach that models method‐dependent threshold values. By a series of simulation studies, we confirm the good performance of the best estimator by comparing it with the other members of its family.     

Non-parametric Curve Estimation by Wavelet Thresholding with Locally Stationary Errors

An important aspect in the modelling of biological phenomena in living organisms, whether the measurements are of blood pressure, enzyme levels, biomechanical movements or heartbeats, etc., is time variation in the data. Thus, the recovery of a 'smooth' regression or trend function from noisy time-varying sampled data becomes a problem of particular interest. Here we use non-linear wavelet thresholding to estimate a regression or a trend function in the presence of additive noise which, in contrast to most existing models, does not need to be stationary. (Here, non-stationarity means that the spectral behaviour of the noise is allowed to change slowly over time). We develop a procedure to adapt existing threshold rules to such situations, e.g. that of a time-varying variance in the errors. Moreover, in the model of curve estimation for functions belonging to a Besov class with locally stationary errors, we derive a near-optimal rate for the -risk between the unknown function and our soft or hard threshold estimator, which holds in the general case of an error distribution with bounded cumulants. In the case of Gaussian errors, a lower bound on the asymptotic minimax rate in the wavelet coefficient domain is also obtained. Also it is argued that a stronger adaptivity result is possible by the use of a particular location and level dependent threshold obtained by minimizing Stein's unbiased estimate of the risk. In this respect, our work generalizes previous results, which cover the situation of correlated, but stationary errors. A natural application of our approach is the estimation of the trend function of non-stationary time series under the model of local stationarity. The method is illustrated on both an interesting simulated example and a biostatistical data-set, measurements of sheep luteinizing hormone, which exhibits a clear non-stationarity in its variance.     

How small cities affect the biodiversity of ground-dwelling mammals and the relevance of this knowledge in planning urban land expansion in terms of urban wildlife

Fundamental principles regarding urban biodiversity are based on studies conducted in large cities. However we cannot know whether the same phenomena occur in smaller cities or how small cities affect biodiversity. Small cities are an inherent element of urbanization and in the future, most global urban growth is expected to take place in small and medium-sized cities. Understanding the effects of small cities on biodiversity will be an important aspect in planning urban land expansion. Our study examined the effects of a small city on communities of small ground-dwelling mammals on 41 sites arranged in a four step gradient of urbanization. In 6700 trap-days, we caught 2333 individuals comprising 15 species. In the downtown area the same phenomena as those described for large cities were observed: a reduction in species richness and diversity, a decline in the abundance of urban sensitive species and an increase in synurbic species. However, in contrast to large city studies, green areas outside the downtown area did not differ from rural sites in small mammal population parameters. This phenomenon of relatively unchanged fauna outside the downtown area shows that small cities have the potential to maintain a high level of diversity of small ground-dwelling mammals if appropriate planning of further building expansion is implemented. More studies of small cities are needed to better assess their impact on biodiversity. This knowledge can then be applied in better planning for urban wildlife. Generalizations based solely on large city studies are inadequate and may lead to incomplete or inappropriate conservation strategies for small cities.     

Empowering Rural Sociology: Exploring and Linking Alternative Paradigms in Theory and Methodology1

Abstract Theoretical and methodological approaches to rural social change are explored, especially those that give visibility to the range of heterogeneous experiences and perspectives that often are overlooked or ignored. Theoretical developments in postmodern, narrative, and feminist theory are described as are the methodological approaches they imply. Examples of research on rural social change that attempt to integrate theory and methods in ways that respect the complicated, processual nature of social life are discussed. They provide concrete illustrations of how alternative approaches can be fruitfully applied to some of the issues and problems rural sociologists typically study.