Weak Convergence of the Regularization Path in Penalized M‐Estimation

Abstract. We consider a function defined as the pointwise minimization of a doubly index random process. We are interested in the weak convergence of the minimizer in the space of bounded functions. Such convergence results can be applied in the context of penalized M‐estimation, that is, when the random process to minimize is expressed as a goodness‐of‐fit term plus a penalty term multiplied by a penalty weight. This weight is called the regularization parameter and the minimizing function the regularization path. The regularization path can be seen as a collection of estimators indexed by the regularization parameter. We obtain a consistency result and a central limit theorem for the regularization path in a functional sense. Various examples are provided, including the ?¹‐regularization path for general linear models, the ?¹‐ or ?²‐regularization path of the least absolute deviation regression and the Akaike information criterion.     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.     

Work of Full‐Time Mothers: Putting Voice to the Relational Theory of Working

The purpose of this qualitative research was to explore the work experience of middle‐class, Korean full‐time mothers in their 50s. Interviews, observations, and photographs were collected from 11 Korean full‐time mothers to understand their work and career experiences. The data were analyzed by a case study qualitative method of inquiry. The themes that emerged from the data were paths to becoming full‐time mothers, multiple working roles from relationships, dialectical characteristics of work, meaning of full‐time mothering, and regrets and internalized biases for full‐time motherhood. The findings illustrate how full‐time mothers experience a sense of meaning and mattering from their work; how relationships and work are intertwined in their lives; and how gender, social class, and culture influence the work and relationships of full‐time mothers. Implications for counselors and researchers are discussed.     

Simplex Mixed‐Effects Models for Longitudinal Proportional Data

Abstract. Continuous proportional outcomes are collected from many practical studies, where responses are confined within the unit interval (0,1). Utilizing Barndorff‐Nielsen and Jørgensen's simplex distribution, we propose a new type of generalized linear mixed‐effects model for longitudinal proportional data, where the expected value of proportion is directly modelled through a logit function of fixed and random effects. We establish statistical inference along the lines of Breslow and Clayton's penalized quasi‐likelihood (PQL) and restricted maximum likelihood (REML) in the proposed model. We derive the PQL/REML using the high‐order multivariate Laplace approximation, which gives satisfactory estimation of the model parameters. The proposed model and inference are illustrated by simulation studies and a data example. The simulation studies conclude that the fourth order approximate PQL/REML performs satisfactorily. The data example shows that Aitchison's technique of the normal linear mixed model for logit‐transformed proportional outcomes is not robust against outliers.     

Exploring the Predictors of Organizational Preparedness for Natural Disasters

There is an extensive body of research on the determinants of disaster preparedness at the individual and household levels. The same cannot be said for the organizational level. Hence, the purpose of this study is to shed light on the predictors of organizational preparedness for natural disasters. Since leaders of organizations have an incentive to overstate their level of preparedness and because surveys of organizational leaders suffer from selection bias and low response rates, we take the novel approach of interviewing employees about the organizations that employ them. Using an online survey, we collected information from a national sample of 2,008 U.S. employees and estimated the predictors of preparedness at the organizational level. We find, among other results, that organization size (facility level) is a consistent predictor of preparedness at the organizational level. We conclude with policy recommendations and outline an agenda for future research on organizational preparedness for natural disasters.     

Politics and Growth

Theories of growth have made progress in understanding the mechanisms of growth in economic terms. However, there is less understanding of the political processes that enable or obstruct these mechanisms. This article provides a four‐stage framework to clarify and analyse the connections between politics and growth: (i) discussing the basic conditions essential for growth; (ii) suggesting that whether or not these conditions emerge depends on specific forms of public‐private interaction; (iii) linking these relationships to the incentives facing those in political power and investors; and (iv) considering the factors at country level that may help to push incentives in a pro‐growth direction.     

New Global Politics of Reproductive Labor: Gendered Labor and Marriage Migration

The feminization of international migration nowadays has demonstrated a new global politics of reproductive labor (work necessary for the reproduction of families). This paper reviews recent studies that manifest similarity, affinity, and continuity across multiple forms of reproductive labor carried out by migrant women in four aspects. First, the recruitment of women as foreign maids or foreign brides provides class‐specific parallel strategies to the global care crisis. Second, paid and unpaid forms of reproductive labor constitute intersecting circuits of labor and marriage migration through which women partake in continuous migration. Third, various categories of migrant women are discursively conflated and attached to similar images as sexualized others. Finally, global care chains not only involve migrant reproductive labor conducted at home but also operate on the level of social reproduction as indicated by the expansion of international nursing migration.     

Nonparametric adaptive likelihood weights

The weighted likelihood can be used to make inference about one population when data from similar populations are available. The author shows heuristically that the weighted likelihood can be seen as a special case of the entropy maximization principle. This leads him to propose the minimum averaged mean squared error (MAMSE) weights. He describes an algorithm for calculating these weights and shows its convergence using the Kuhn‐Tucker conditions. He explores the performance and properties of the weighted likelihood based on MAMSE weights through simulations.