Bayesian network structural learning from complex survey data: a resampling based approach

Statistical Methods & Applications - Nowadays there is increasing availability of good quality official statistics data. The construction of multivariate statistical models possibly leading to...     

Effort Observability,Incentive Systems,and Organizational Forms

This paper analyses the choice between a centralized and a decentralized organizational structure focusing on the relationship existing between the allocation of decision rights, the degree of observability of effort and the use of different compensation systems. Profitable production is realized thanks to good information being available during the project selection stage and hard work being performed in the implementation stage. Selection of projects may be made by the principal (hierarchy) or by the agent (delegation). Under the hierarchical system, the principal's participation in the design of projects allows her to gather useful information for the design of a more accurate compensation system. Therefore, we analyse the trade‐off between optimal use of available information and accuracy of incentive systems, which emerges in the choice of organizational form.     

Bayesian sample size determination for phase IIA clinical trials using historical data and semi‐parametric prior's elicitation

The Simon's two‐stage design is the most commonly applied among multi‐stage designs in phase IIA clinical trials. It combines the sample sizes at the two stages in order to minimize either the expected or the maximum sample size. When the uncertainty about pre‐trial beliefs on the expected or desired response rate is high, a Bayesian alternative should be considered since it allows to deal with the entire distribution of the parameter of interest in a more natural way. In this setting, a crucial issue is how to construct a distribution from the available summaries to use as a clinical prior in a Bayesian design. In this work, we explore the Bayesian counterparts of the Simon's two‐stage design based on the predictive version of the single threshold design. This design requires specifying two prior distributions: the analysis prior, which is used to compute the posterior probabilities, and the design prior, which is employed to obtain the prior predictive distribution. While the usual approach is to build beta priors for carrying out a conjugate analysis, we derived both the analysis and the design distributions through linear combinations of B‐splines. The motivating example is the planning of the phase IIA two‐stage trial on anti‐HER2 DNA vaccine in breast cancer, where initial beliefs formed from elicited experts' opinions and historical data showed a high level of uncertainty. In a sample size determination problem, the impact of different priors is evaluated.     

Absenteeism and peer interaction effects: Evidence from an Italian Public Institute

Using microdata for a sample of about 350 workers employed by an Italian public institute, we explain individual absence rates considering both variables that may be related to health conditions and variables that may suggest shirking behaviour. Among these variables we especially focus our attention on the influence produced by the behaviour of randomly assigned peers. In order to handle reflection problems, we combine random assignment with the use of instrumental variables. The proportion of females in the peer group is used as an instrument of peer absence behaviour. From Two-Stage Least Square estimates it emerges that social and group interaction plays an important role in shaping individual absence behaviour.     

Sample size calculation for recurrent events data in one‐arm studies

In some exceptional circumstances, as in very rare diseases, nonrandomized one‐arm trials are the sole source of evidence to demonstrate efficacy and safety of a new treatment. The design of such studies needs a sound methodological approach in order to provide reliable information, and the determination of the appropriate sample size still represents a critical step of this planning process. As, to our knowledge, no method exists for sample size calculation in one‐arm trials with a recurrent event endpoint, we propose here a closed sample size formula. It is derived assuming a mixed Poisson process, and it is based on the asymptotic distribution of the one‐sample robust nonparametric test recently developed for the analysis of recurrent events data. The validity of this formula in managing a situation with heterogeneity of event rates, both in time and between patients, and time‐varying treatment effect was demonstrated with exhaustive simulation studies. Moreover, although the method requires the specification of a process for events generation, it seems to be robust under erroneous definition of this process, provided that the number of events at the end of the study is similar to the one assumed in the planning phase. The motivating clinical context is represented by a nonrandomized one‐arm study on gene therapy in a very rare immunodeficiency in children (ADA‐SCID), where a major endpoint is the recurrence of severe infections. Copyright © 2012 John Wiley & Sons, Ltd.     

秘鲁城市女性劳动参与的微观研究

本文利用2001年秘鲁国家统计和信息局第三期家庭抽样调查数据,运用Logistic回归,分析了在秘鲁城市中,人口、经济和教育变量是如何影响女性劳动参与的.并且将首都利马和利马以外的其他地区加以区分来进行分析。研究结果表明在秘鲁城市范围内.女性劳动参与随着女性生命周期的变化出现了很明确的模式;生育、婚姻状况、与家长的关系、接受高等教育(指硕士及以上和培训)明显地影响女性劳动参与。在首都利马和其他地区的大部分影响方向很类似。但是在首都利马的影响程度要大一些。     

Stochastic Choice and Consideration Sets

We model a boundedly rational agent who suffers from limited attention. The agent considers each feasible alternative with a given (unobservable) probability, the attention parameter, and then chooses the alternative that maximizes a preference relation within the set of considered alternatives. We show that this random choice rule is the only one for which the impact of removing an alternative on the choice probability of any other alternative is asymmetric and menu independent. Both the preference relation and the attention parameters are identified uniquely by stochastic choice data.     

Is Infant Initiation of Joint Attention by Pointing Affected by Type of Interaction?

This article reports the results of two experiments studying the effects of type of interaction on infant production of declarative pointing. In Experiment 1, intensity of social presence was manipulated in adult–infant interaction with 12–19-month-olds (no social presence; adult responding only; adult also initiating joint attentional bids). Experiment 2 extended the analysis to peer interaction in 12–24-month-olds. Pointing was considered in the context of other gestures, vocalizations, and visual checking. The results show that pointing, but not vocalizing, virtually disappeared in the non-social condition, whereas differential effects between the adult-responding and initiating conditions were limited. Joint attentional bids were produced in peer interaction even in the younger age group, although overall the frequency of gestures was lower than that in adult–infant interaction. The results are discussed in the context of the relationship between communication and the infant developing social cognition.     

Evaluating the impact of a grouping variable on Job Satisfaction drivers

An important issue in Job Satisfaction analysis is to discover the most important drivers of the workers’ overall satisfaction. This can be investigated by means of data mining techniques able to measure the importance of a covariate in the prediction of a given outcome. Variable importance measures are mainly proposed in the literature in the framework of tree-based learning ensembles, like Random Forests or Gradient Boosting Machine. In this paper a Random Forest variable importance measure is used for mining the drivers of Job Satisfaction in the Social Service sector. In addition an innovative algorithmic procedure is proposed in order to assess the impact of a grouping variable on this variable importance measure. The goal is to investigate if the importance of a Job Satisfaction driver is different for subjects belonging to different groups.