Parallel inference for massive distributed spatial data using low-rank models

Due to rapid data growth, statistical analysis of massive datasets often has to be carried out in a distributed fashion, either because several datasets stored in separate physical locations are all relevant to a given problem, or simply to achieve faster (parallel) computation through a divide-and-conquer scheme. In both cases, the challenge is to obtain valid inference that does not require processing all data at a single central computing node. We show that for a very widely used class of spatial low-rank models, which can be written as a linear combination of spatial basis functions plus a fine-scale-variation component, parallel spatial inference and prediction for massive distributed data can be carried out exactly, meaning that the results are the same as for a traditional, non-distributed analysis. The communication cost of our distributed algorithms does not depend on the number of data points. After extending our results to the spatio-temporal case, we illustrate our methodology by carrying out distributed spatio-temporal particle filtering inference on total precipitable water measured by three different satellite sensor systems.     

Bisphenol A: How the Most Relevant Exposure Sources Contribute to Total Consumer Exposure

Bisphenol A (BPA) is an endocrine disrupting chemical that is found in human urine throughout industrial societies around the globe. Consumer exposure pathways to BPA include packaged food, household dust, air, and dental fillings. To date, information on the relative contribution of the different pathways to total consumer exposure is lacking, but is key for managing substance‐associated risks. We investigated the relative contributions of the pathways known to be most relevant for nine different consumer groups. Our results suggest that the most important pathways for infants and children are the use of polycarbonate (PC) baby bottles and for adults and teenagers the consumption of canned food. Dental surgery can also considerably contribute over a short time directly after the surgery. For infants fed with PC baby bottles with mean dose rates of 0.8 μg/kg_bw/d the highest exposure dose rate was calculated. This dose rate is far below the tolerable daily intake of 50 μg/kg_bw/d. However, it is of the same order of magnitude as recently reported concentrations that caused low‐dose health effects in rodents. We find a pattern of falling exposure levels with rising age that is supported by urinary concentrations of BPA available for selected consumer groups. Similarly, the exposure levels we predict are confirmed by the levels reported in these studies.     

Management consultancy’s role in delivering lasting [triple bottom line] benefits

Abstract

Management consultants have become ubiquitous in helping improve organizational performance. This paper presents an investigation of the impact of their interventions on organizational sustainability and growth through the performance improvement work carried out for and with their clients. The paper presents the findings of a questionnaire survey of 440 respondents from 206 countries; 197 of respondents were Small and Medium Sized Enterprises (SMEs) and 243 from large organizations. There is a particular focus on knowledge transfer in terms of urgency and impact of the work with regard to the extent to which consulting interventions in SMEs, as well as large multinational corporations, embed long-term sustainability practices.     

Sektion Soziologie und Ökologie

Ohne Zusammenfassung     

Joint Income-Wealth Inequality: Evidence from Lucerne Tax Data

Social Indicators Research - Using tax data from the Swiss canton of Lucerne, we study how measures of economic inequality change if they account for income and wealth rather than income alone....     

A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems

The analysis of survival endpoints subject to right-censoring is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semiparametric models are the proportional hazards model and the accelerated failure time (AFT) model. Rank-based estimation in the AFT model is computationally challenging due to optimization of a non-smooth loss function. Previous work has shown that rank-based estimators may be written as solutions to linear programming (LP) problems. However, the size of the LP problem is O(n ²+p) subject to n ² linear constraints, where n denotes sample size and p denotes the dimension of parameters. As n and/or p increases, the feasibility of such solution in practice becomes questionable. Among data mining and statistical learning enthusiasts, there is interest in extending ordinary regression coefficient estimators for low-dimensions into high-dimensional data mining tools through regularization. Applying this recipe to rank-based coefficient estimators leads to formidable optimization problems which may be avoided through smooth approximations to non-smooth functions. We review smooth approximations and quasi-Newton methods for rank-based estimation in AFT models. The computational cost of our method is substantially smaller than the corresponding LP problem and can be applied to small- or large-scale problems similarly. The algorithm described here allows one to couple rank-based estimation for censored data with virtually any regularization and is exemplified through four case studies.