Radon as a Tracer of Lung Changes Induced by Smoking

After smoking, exposure to radon and its progeny is the second leading cause of lung cancer. The probability of inducing lung carcinomas by inhaled radon progeny depends on the deposited radiation dose, and is significantly affected by physiological and morphometric changes induced by smoking. Due to irritation of the airways, the inhalation of cigarette smoke leads to the hyperproduction of mucus. Two concurrent processes occur: on one hand, increased production of mucus protects the target cells against radiation damage; on the other hand, in the case of long-term smokers, a chronic lung obstruction develops, causing an increase in the radiation dose to the lungs. Depending on the duration and intensity of smoking, these processes contribute to the final radiation dose with different weights. The primary objective of this study was to investigate to what extent these smoke-induced changes can modify the resulting absorbed dose of inhaled radon progeny relative to healthy nonsmokers. Since the bronchial dose depends on the degree of lung tissue damage, we have used this dose as a tool for detecting the effects of smoking on the lung epithelium. In other words, the biological effect of radon served as a tracer of changes induced by smoking.     

Non-rejective implementations of the Sampford sampling design

Sampford sampling is a method for unequal probability sampling. There exist several implementations of the Sampford sampling design which all are rejective methods, i.e. the sample is not always accepted. Thus the existing methods can be time consuming or even infeasible in some situations. In this paper, a fast and non-rejective list-sequential method, which works in all situations, is presented. The method is a modification of a previously existing rejective list-sequential method. Another list-sequential implementation of Sampford sampling is also presented.     

Modelling of a thermomechanically coupled forming process based on functional outputs from a finite element analysis and from experimental measurements

In this paper, measurements from experiments and results of a finite element analysis (FEA) are combined in order to compute accurate empirical models for the temperature distribution before a thermomechanically coupled forming process. To accomplish this, Design and Analysis of Computer Experiments (DACE) is used to separately compute models for the measurements and the functional output of the FEA. Based on a hierarchical approach, a combined model of the process is computed. In this combined modelling approach, the model for the FEA is corrected by taking into account the systematic deviations from the experimental measurements. The large number of observations based on the functional output hinders the direct computation of the DACE models due to the internal inversion of the correlation matrix. Thus, different techniques for identifying a relevant subset of the observations are proposed. The application of the resulting procedure is presented, and a statistical validation of the empirical models is performed.     

Consistency of the GMLE with Mixed Case Interval-Censored Data

In this paper we consider an interval censorship model in which the endpoints of the censoring intervals are determined by a two stage experiment. In the first stage the value k of a random integer is selected; in the second stage the endpoints are determined by a case k interval censorship model. We prove the strong consistency in the L ₁( μ )-topology of the non-parametric maximum likelihood estimate of the underlying survival function for a measure μ which is derived from the distributions of the endpoints. This consistency result yields strong consistency for the topologies of weak convergence, pointwise convergence and uniform convergence under additional assumptions. These results improve and generalize existing ones in the literature.     

Entropy of unequal probability sampling designs

There exist many designs for unequal probability sampling. In this paper entropy, which is a measure of randomness, is used to compare eight designs. Both old and commonly used designs and more recent designs are included. Several different and general estimates of entropy are presented. In the quest of finding entropy, expressions for the probability function are derived for different designs. One of them is a recent general design called correlated Poisson sampling. Several designs are close to having maximum entropy, which means that the designs are robust. A few designs yield low entropy and should therefore in general be avoided.     

Dynamic Pricing in the Presence of Myopic and Strategic Consumers: Theory and Experiment

We investigate retailers’ dynamic pricing decisions in a stylized two‐period setting with possible supply constraints and demand from both myopic and strategic consumers. We present an analytical model and then test its predictions in a behavioral experiment in which human subjects played the role of pricing managers. We find that the fraction of strategic consumers in the market systematically moderates the optimal pricing structure. When this fraction exceeds a certain threshold, the retailer offers relatively small late season markdowns to discourage strategic consumers from waiting and to incentivize them to buy during the early season; otherwise, the retailer offers relatively large markdowns to divert all strategic consumers to the late season, where the majority of revenue is made. Our model analyses suggest that the latter policy is optimal under fairly broad conditions. Our experiment shows that after some significant learning, aggregate behavior is able to approximate the key qualitative predictions from our model analysis, with one notable deviation: in the presence of a mixture of myopic and strategic consumers, subjects act somewhat myopically – they underprice and oversell in the main selling season, which significantly limits their ability to generate revenue in the markdown season.     

Overcoming barriers: Effects of entering vocational rehabilitation on labour market outcomes

Vocational rehabilitation aims at getting people with health problems or disabilities back into work. In Germany, the Public Employment Service is a central provider of vocational rehabilitation services targeted at unemployed people. Against this background, the study examined the labour market effects of vocational rehabilitation for people registered as unemployed with the Public Employment Service. A comparison of the accepted and the rejected applicants for vocational rehabilitation using inverse probability weighting based on propensity scores provides an insight into its effects. The results suggest that the employment prospects of persons who were accepted in the programme improved modestly after the third‐year following application. A subgroup analysis identified stronger re‐employment effects of acceptance into vocational rehabilitation in eastern Germany as well as for men and older individuals.     

The choice among past trends as a basis for the prediction of future trends in old-age mortality

We explored the extent to which projections of future old-age mortality trends differ when different projection bases are used. For seven European countries, four alternative sets of annual rates of mortality change were estimated with age-period log-linear regression models, and subsequently applied to age-specific all-cause mortality rates (80+) in 1999 to predict mortality levels up to 2050. On average, up to 2050, e80 is predicted to increase further by 2.33 years among men and 4.03 years among women. Choosing a historical period of 25 instead of 50 years results in higher predicted gains in e80 for men but lower gains for women. Choosing non-smoking-related mortality instead of all-cause mortality leads to higher gains for women and mixed results for men. In all alternatives there is a strong divergence of predicted mortality levels between the countries. Future projections should be preceded by a thorough study of past trends and their determinants.