Visualizing uncertainty in reliability functions with application to aero engine overhaul

Summary.  We propose an approach for estimating the date of lost confidence of jet engines, which are devices with multiple components subject to disruption. A mixed Weibull distribution is estimated from a large data set subject to censoring at various times. Parametric uncertainty is derived analytically and mapped visually onto the functions of use in reliability theory, including the hazard function. We demonstrate the use of the method on a database of disruption times for components in 325 jet engines.     

The Significance of Education for Mortality Compression in the United States

Recent studies of old-age mortality trends assess whether longevity improvements over time are linked to increasing compression of mortality at advanced ages. The historical backdrop of these studies is the long-term improvement in a population's socioeconomic resources that fueled longevity gains. We extend this line of inquiry by examining whether socioeconomic differences in longevity within a population are accompanied by old-age mortality compression. Specifically, we document educational differences in longevity and mortality compression for older men and women in the United States. Drawing on the fundamental cause of disease framework, we hypothesize that both longevity and compression increase with higher levels of education and that women with the highest levels of education will exhibit the greatest degree of longevity and compression. Results based on the Health and Retirement Study and the National Health Interview Survey Linked Mortality File confirm a strong educational gradient in both longevity and mortality compression. We also find that mortality is more compressed within educational groups among women than men. The results suggest that educational attainment in the United States maximizes life chances by delaying the biological aging process.     

Chebyshev's inequality for nonparametric testing with small N and α in microarray research

Summary.  Microarrays are a powerful new technology that allow for the measurement of the expression of thousands of genes simultaneously. Owing to relatively high costs, sample sizes tend to be quite small. If investigators apply a correction for multiple testing, a very small p -value will be required to declare significance. We use modifications to Chebyshev's inequality to develop a testing procedure that is nonparametric and yields p -values on the interval [0, 1]. We evaluate its properties via simulation and show that it both holds the type I error rate below nominal levels in almost all conditions and can yield p -values denoting significance even with very small sample sizes and stringent corrections for multiple testing.     

走向一种幸福经济学

对当前经济发展人们的幸福指数却在降低的现实及其原因进行了分析,提出了经济的发展最高目标是幸福的观点,认为应该以对生活最重视的东西为标准来界定和衡量进步,而不是以GDP这样的狭义货币增长措施为标准,并对中国致力于和谐发展给予了充分的肯定。     

Modeling the Effects of Genetic Factors on Late-Onset Diseases in Cohort Studies

Many late-onset diseases are caused by what appears to be a combination of a genetic predisposition to disease and environmental factors. The use of existing cohort studies provides an opportunity to infer genetic predisposition to disease on a representative sample of a study population, now that many such studies are gathering genetic information on the participants. One feature to using existing cohorts is that subjects may be censored due to death prior to genetic sampling, thereby adding a layer of complexity to the analysis. We develop a statistical framework to infer parameters of a latent variables model for disease onset. The latent variables model describes the role of genetic and modifiable risk factors on the onset ages of multiple diseases, and accounts for right-censoring of disease onset ages. The framework also allows for missing genetic information by inferring a subject's unknown genotype through appropriately incorporated covariate information. The model is applied to data gathered in the Framingham Heart Study for measuring the effect of different Apo-E genotypes on the occurrence of various cardiovascular disease events.     

Economic concentration and corporate political behavior: A cross-industry comparison

Several political sociologists have argued that the trend toward economic concentration in major industries in advanced capitalist societies has led to a growth in corporate political power. Most of these arguments are based on the notion that economic concentration facilitates the mobilization of industries as cohesive politica actors. This paper presents an empirical examination of this argument. The similarity of campaign contributions in the 1980 Congressional elections by the four largest members of 25 industries is employed as the dependent variable. Concentration ratios within the industries, sizes and geographical proximities of the firms, and measures of within-industry economic and social integration are employed as independent and intervening variables. Employing a spatial autocorrelation model to correct for nonindependence among observations, we find that although concentration and similarity of political behavior are positively associated in a bivariate correlation, the relation disappears when controls are introduced. Common stock ownership by the same institutions and indirect director interlocks through financial institutions are significant predictors of behavioral similarity.     

CAUSAL GRAPHICAL MODELS IN SYSTEMS GENETICS: A UNIFIED FRAMEWORK FOR JOINT INFERENCE OF CAUSAL NETWORK AND GENETIC ARCHITECTURE FOR CORRELATED PHENOTYPES

Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenotype network structure. In this paper we develop a QTL-driven phenotype network method (QTLnet) to jointly infer a causal phenotype network and associated genetic architecture for sets of correlated phenotypes. Randomization of alleles during meiosis and the unidirectional influence of genotype on phenotype allow the inference of QTLs causal to phenotypes. Causal relationships among phenotypes can be inferred using these QTL nodes, enabling us to distinguish among phenotype networks that would otherwise be distribution equivalent. We jointly model phenotypes and QTLs using homogeneous conditional Gaussian regression models, and we derive a graphical criterion for distribution equivalence. We validate the QTLnet approach in a simulation study. Finally, we illustrate with simulated data and a real example how QTLnet can be used to infer both direct and indirect effects of QTLs and phenotypes that co-map to a genomic region.     

Parameter Estimation in Large Dynamic Paired Comparison Experiments

Paired comparison data in which the abilities or merits of the objects being compared may be changing over time can be modelled as a non-linear state space model. When the population of objects being compared is large, likelihood-based analyses can be too computationally cumbersome to carry out regularly. This presents a problem for rating populations of chess players and other large groups which often consist of tens of thousands of competitors. This problem is overcome through a computationally simple non-iterative algorithm for fitting a particular dynamic paired comparison model. The algorithm, which improves over the commonly used algorithm of Elo by incorporating the variability in parameter estimates, can be performed regularly even for large populations of competitors. The method is evaluated on simulated data and is applied to ranking the best chess players of all time, and to ranking the top current tennis-players.     

教育研究中比较的层次问题

本文认为比较教育传统上以国别和世界地区为分析单位容易导致不平衡和不全面的观点，指出有必要建立更加宽泛的比较教育概念范围，提倡并论证了多层次比较分析作为一种研究形式所具备的综合整理各单一层次分析结果的价值所在。