Hybrid networks and the global politics of the digital revolution – a practice‐oriented,relational and agnostic approach

The rapid growth of internet users and the importance of networked technologies for most spheres of life raise questions about how to foster and govern the digital revolution on a global scale. Focusing on internet governance and the use of ICTs for development purposes, I provide a multi‐sited, ethnographic exploration of two UN‐based multi‐stakeholder arrangements – comprising governments, business and civil society groups – that have contributed to the construction of the digital revolution as an object of global governance. In this article I show how analytical insights from governmentality studies and actor‐network theory can be used to capture how objects of governance and organizational arrangements are constructed and consolidated. Conventional approaches to networks and governance tend to treat organizational arrangements and issue areas as bounded, separate and fixed. By contrast, I demonstrate the merits of a practice‐oriented, relational and agnostic research strategy, which foregrounds the governmental techniques and moments of translation involved when new objects and modes of governance are assembled and negotiated.     

Sticky Price Models of the Business Cycle: Can the Contract Multiplier Solve the Persistence Problem?

We construct a quantitative equilibrium model with firms setting prices in a staggered fashion and use it to ask whether monetary shocks can generate business cycle fluctuations. These fluctuations include persistent movements in output along with the other defining features of business cycles, like volatile investment and smooth consumption. We assume that prices are exogenously sticky for a short time. Persistent output fluctuations require endogenous price stickiness in the sense that firms choose not to change prices much when they can do so. We find that for a wide range of parameter values, the amount of endogenous stickiness is small. Thus, we find that in a standard quantitative model, staggered price‐setting, alone, does not generate business cycle fluctuations.     

Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality

We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, welfare, and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether poverty, inequality, or social welfare is greater in one distribution than in another for general classes of indices and for ranges of possible poverty lines. We also derive the sampling distribution of the maximal poverty lines up to which we may confidently assert that poverty is greater in one distribution than in another. The sampling distribution of convenient dual estimators for the measurement of poverty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for four countries drawn from the Luxembourg Income Study data bases.     

Child protection: a new approach in South Australia

This paper argues the case for an integrated approach to child protection services, embracing children's safety, risk and needs and incorporating formal assessment instruments. It then provides a brief overview of the new child protection system in South Australia. The steady rise in child abuse/neglect reports raises questions about how best to provide child protection services. Analysis of South Australian data suggests that much of the rise can be attributed to an increase in reports of neglect and emotional abuse and an increase in re‐notifications. This in turn raises questions about the traditional investigative approach. In 1995 Messages from Research advocated that agencies re‐focus on children's needs and concentrate less on investigations into safety and risk. The critical question is how to distinguish families requiring investigation from those needing support. The latest research suggests that such decision‐making in child protection is generally inconsistent and unreliable. This paper argues that statutory agencies should use formal assessment tools to determine the level and nature of their interventions. These arguments have been critical in the development of the new child protection model in SA, which relies on a centralized intake, differential response to reports and the structured decision‐making system. Initial evaluation of the new model indicates improvements in consistency of initial screening and responses to children in danger. Copyright © 1999 John Wiley & Sons, Ltd.     

ESTIMATION OF MULTIVARIATE SHANNON ENTROPY USING MOMENTS

Three new entropy estimators of multivariate distributions are introduced. The two cases considered here concern when the distribution is supported by a unit sphere and by a unit cube. In the former case, the consistency and the upper bound of the absolute error for the proposed entropy estimator are established. In the latter one, under the assumption that only the moments of the underlying distribution are available, a non‐traditional estimator of the entropy is suggested. We also study the practical performances of the constructed estimators through simulation studies and compare the estimators based on the moment‐recovered approaches with their counterparts derived by using the histogram and k th nearest neighbour constructions. In addition, one worked example is briefly discussed.     

Adjusted empirical likelihood models with estimating equations for accelerated life tests

This article proposes an adjusted empirical likelihood estimation (AMELE) method to model and analyze accelerated life testing data. This approach flexibly and rigorously incorporates distribution assumptions and regression structures by estimating equations within a semiparametric estimation framework. An efficient method is provided to compute the empirical likelihood estimates, and asymptotic properties are studied. Real-life examples and numerical studies demonstrate the advantage of the proposed methodology.     

Two‐sample empirical likelihood ratio tests for medians in application to biomarker evaluations

The median is a commonly used parameter to characterize biomarker data. In particular, with two vastly different underlying distributions, comparing medians provides different information than comparing means; however, very few tests for medians are available. We propose a series of two‐sample median‐specific tests using empirical likelihood methodology and investigate their properties. We present the technical details of incorporating the relevant constraints into the empirical likelihood function for in‐depth median testing. An extensive Monte Carlo study shows that the proposed tests have excellent operating characteristics even under unfavourable occasions such as non‐exchangeability under the null hypothesis. We apply the proposed methods to analyze biomarker data from Western blot analysis to compare normal cells with bronchial epithelial cells from a case–control study. The Canadian Journal of Statistics 39: 671–689; 2011. © 2011 Statistical Society of Canada     

A note on testing the regression functions via nonparametric smoothing

The authors present a consistent lack‐of‐fit test in nonlinear regression models. The proposed procedure possesses some nice properties of Zheng's test such as the consistency, the ability to detect any local alternatives approaching the null at rates slower than the parametric rate. What's more, for a predetermined kernel function, the proposed test is more powerful than Zheng's test and the validity of these findings is confirmed by the simulation studies and a real data example. In addition, the authors find out a close connection between the choices of normal kernel functions and the bandwidths. The Canadian Journal of Statistics 39: 108–125; 2011 © 2011 Statistical Society of Canada     

Estimation of the bivariate cause-specific distribution function with doubly censored competing risks data

In this article, we consider estimating the bivariate cause-specific distribution function when both components are subject to double censoring. We propose two types of estimators as generalizations of the Dabrowska and Campbell and Földes estimators. The asymptotical properties of the proposed estimators are established. A simulation study is conducted to investigate the performance of the proposed estimators.     

A note on Läuter-type robust design for polynomial regression models

In this paper, we consider the problem of model robust design for simultaneous parameter estimation among a class of polynomial regression models with degree up to k. A generalized D-optimality criterion, the Ψ_α‐optimality criterion, first introduced by Läuter (1974) is considered for this problem. By applying the theory of canonical moments and the technique of maximin principle, we derive a model robust optimal design in the sense of having highest minimum Ψ_α‐efficiency. Numerical comparison indicates that the proposed design has remarkable performance for parameter estimation in all of the considered rival models.