Collective versus Individual Effects in Survival Analysis of Multiple Failures

In survival analysis, we sometimes encounter data with multiple censored outcomes. Under certain scenarios, partial or even all covariates have ‘similar’ relative risks on the multiple outcomes in the Cox regression analysis. The similarity in covariate effects can be quantified using the proportionality of regression coefficients. Identifying the proportionality structure, or equivalently whether covariates have individual or collective effects, may have important scientific implications. In addition, it can lead to a smaller set of unknown parameters, which in turn results in more accurate estimation. In this article, we develop a novel approach for identifying the proportionality structure. Simulation shows the satisfactory performance of the proposed approach and its advantage over estimation under no assumed structure. We analyse three datasets to demonstrate the practical application of the proposed approach.     

Investigating impacts of complex sampling on latent growth curve modelling

We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.     

Cultural capital or relative risk aversion? Two mechanisms for educational inequality compared1

In this paper we empirically examined two explanatory mechanisms for educational inequality: cultural reproduction and relative risk aversion, using survey data taken from secondary school pupils in Amsterdam. Cultural reproduction theory seeks to explain class variations in schooling by cultural differences between social classes. Relative risk aversion theory argues that educational inequalities can be understood by between-class variation in the necessity of pursuing education at branching points in order to avoid downward mobility. We showed that class variations in early demonstrated ability are for a substantial part cultural: cultural capital - measured by parental involvement in highbrow culture - affected school performance at the primary and secondary level. However, relative risk aversion - operationalized by being concerned with downward mobility - strongly affects schooling ambitions, whereas cultural capital had no effect. Thus, we conclude that 'primary effects' of social origin on schooling outcomes are manifested through cultural capital and not through relative risk aversion (in addition to other potential sources of class variations such as genetics). Relative risk aversion, and not cultural capital, affects schooling ambitions, which is relevant for our understanding of secondary effects.     

An Experiment on the Social Facilitation of Gambling Behavior

Research and theory regarding the social facilitation effect generates the expectation that the presence of other gamblers (or co-actors) in a gaming venue is likely to intensify individual gambling behavior and magnify losses. Fifty male and 66 female participants (116 total) played a computer-simulated electronic gaming machine with a fixed winning sequence, followed by an indefinite losing sequence. Measures of the intensity of gambling behavior included the final payout (a direct measure of losses), average bet-size, number of trials played, and the speed of play. Some participants received false feedback from the computer designed to suggest that other gamers in adjacent rooms were playing and sometimes winning at the same game. Persons who received both sight and sound information, including winning bells and instant messages regarding the wins of other (fake) players, placed more bets and lost more money compared to the other conditions with less information.     

老年药品广告、消费市场与有限效果

随着我国社会老龄化进程的加速，老年人健康问题日益受到关注。老年群体的庞大基数、高发病率带来的药品市场的巨大需求刺激了老年常用药品广告的迅速繁荣。老年人作为消费主体的特殊性及药品作为消费品的特殊性，使老年常用药品广告具有独特的研究价值。以南京市有交流能力的老年人为样本总体，采用问卷为主、访谈为辅的研究方式进行实证性抽样调查。研究发现，老年常用药品广告承受着老年群体特质所导致的权威崇拜与经验强势的双重阻力，在其有限效果的背后，蕴含源自老年受众群体特质的深层原因。     

略论不动产善意取得制度的法律效果

不动产善意取得法律效果表现为善意第三人取得相应物权,包括了物权取得本身,还包括所有权的负担情况以及其他物权的顺位情况。原权利人与无权处分人之间或与有过错的登记机关之间产生债权关系,原权利人可依法向相关责任人请求赔偿损失。     

Confidence intervals for treatment effect from restricted maximum likelihood

An explicit form of confidence intervals for the treatment effect in random effects meta-analysis model obtained from Harville–Jeske–Kenward–Roger approach is given. These restricted likelihood based intervals are compared to alternative procedures commonly used in collaborative studies when the number of participants is small and study-specific variances are heterogeneous. Monte Carlo simulation experiments show that the former intervals have quite conservative coverage probabilities and favor the latter intervals.     

Estimation and Inference for Linear Panel Data Models Under Misspecification When Both n and T are Large

This article considers fixed effects (FE) estimation for linear panel data models under possible model misspecification when both the number of individuals, n, and the number of time periods, T, are large. We first clarify the probability limit of the FE estimator and argue that this probability limit can be regarded as a pseudo-true parameter. We then establish the asymptotic distributional properties of the FE estimator around the pseudo-true parameter when n and T jointly go to infinity. Notably, we show that the FE estimator suffers from the incidental parameters bias of which the top order is O(T^{? 1}), and even after the incidental parameters bias is completely removed, the rate of convergence of the FE estimator depends on the degree of model misspecification and is either (nT)^{? 1/2} or n^{? 1/2}. Second, we establish asymptotically valid inference on the (pseudo-true) parameter. Specifically, we derive the asymptotic properties of the clustered covariance matrix (CCM) estimator and the cross-section bootstrap, and show that they are robust to model misspecification. This establishes a rigorous theoretical ground for the use of the CCM estimator and the cross-section bootstrap when model misspecification and the incidental parameters bias (in the coefficient estimate) are present. We conduct Monte Carlo simulations to evaluate the finite sample performance of the estimators and inference methods, together with a simple application to the unemployment dynamics in the U.S.