旅行社产品市场逆向选择研究

旅行社产品的无形性、生产和消费的同时性等特征使旅游者和旅行社之间存在着信息不对称现象,造成旅行社产品市场逆向选择。解决旅行社产品市场逆向选择的关键在于使消费者获得真实、全面的产品信息。通过旅行社向消费者传递产品信息,将赔偿金写入合同来发送信号以及加强政府及第三方管制等途径,可以改善旅行社产品市场逆向选择现象,使消费者和旅行社的利益都得到保障。     

科技行为选择的前瞻性责任伦理

在目前的科技中性论、科技分别论、科技选择论等争论中,"科技选择论"认为科学家对科技行为的选择总是要承担一定的道德责任的,因而该说较为客观.基于此说,我们有必要对科学家提出一种新的责任意识,即以未来行为为导向的前瞻性责任伦理.明确前瞻性责任意识,科学家要有行为选择的自律精神,社会对科学家也要实行道德法制的他律约束,这在新科技环境下具有重要的操作性意义.     

Trust and partner selection in social networks: An experimentally grounded model

This article investigates the importance of the endogenous selection of partners for trust and cooperation in market exchange situations, where there is information asymmetry between investors and trustees. We created an experimental-data driven agent-based model where the endogenous link between interaction outcome and social structure formation was examined starting from heterogeneous agent behaviour. By testing various social structure configurations, we showed that dynamic networks lead to more cooperation when agents can create more links and reduce exploitation opportunities by free riders. Furthermore, we found that the endogenous network formation was more important for cooperation than the type of network. Our results cast serious doubt about the static view of network structures on cooperation and can provide new insights into market efficiency.     

On Testing Sample Selection Bias Under the Multicollinearity Problem

This paper reviews and extends the literature on the finite sample behavior of tests for sample selection bias. Monte Carlo results show that, when the “multicollinearity problem” identified by Nawata (1993) is severe, (i) the t-test based on the Heckman-Greene variance estimator can be unreliable, (ii) the Likelihood Ratio test remains powerful, and (iii) nonnormality can be interpreted as severe sample selection bias by Maximum Likelihood methods, leading to negative Wald statistics. We also confirm previous findings (Leung and Yu, 1996) that the standard regression-based t-test (Heckman, 1979) and the asymptotically efficient Lagrange Multiplier test (Melino, 1982), are robust to nonnormality but have very little power.     

Summer temperature effects on deaths and hospital admissions among the elderly population in two Italian cities

In developed countries the effects of climate on health status are mainly due to temperature. Our analysis is aimed to deepen statistically the relationship between summer climate conditions and daily frequency of health episodes: deaths or hospital admissions. We expect to find a U-shaped relationship between temperature and frequencies of events occurring in summer regarding the elderly population resident in Milano and Brescia. We use as covariates hourly records of temperature recorded at observation sites located in Milano and Brescia. The analysis is performed using Generalized Additive Models (GAM), where the response variable is the daily number of events, which varies as a possibly non-linear function of meteorological variables measured on the same or previous day. We consider separate models for Milano and Brescia and then we compare temperature effects among the two towns and among different age classes. Moreover we consider separate models for all diagnosed events, for those due to respiratory disease and those due to circulatory pathologies. Model selection is a central problem, the basic methods used are the UBRE and GCV criteria but, instead of conditioning all final conclusions on the best model according to the chosen criterion, we investigated the effect of model selection by implementing a bootstrap procedure.     

低生育水平与出生性别比偏高的后果

自 8 0年代中期以来 ,我国出生性别比出现了明显偏高且持续上升的趋势。这引起了国内外广泛的关注与担心。文章分析了低生育水平与出生性别比的关系 ,并对我国出生性别比偏高的婚姻后果进行了定量分析和预测 ,从而纠正了一些不正确的观点与认识。     

论博尔赫斯作品中的性、失明与巴洛克风格

作为博尔赫斯最基本的隐喻，性、失明与巴洛克风格也是他写作策略的一部分。通过对它们的呈现或变形，博尔赫斯逐步切近其先验世界，同时又使其拥有增殖与自发性。     

Model-averaged ℓ1 regularization using Markov chain Monte Carlo model composition

Bayesian model averaging (BMA) is an effective technique for addressing model uncertainty in variable selection problems. However, current BMA approaches have computational difficulty dealing with data in which there are many more measurements (variables) than samples. This paper presents a method for combining ?₁ regularization and Markov chain Monte Carlo model composition techniques for BMA. By treating the ?₁ regularization path as a model space, we propose a method to resolve the model uncertainty issues arising in model averaging from solution path point selection. We show that this method is computationally and empirically effective for regression and classification in high-dimensional data sets. We apply our technique in simulations, as well as to some applications that arise in genomics.     

Investigation about a screening step in model selection

In many studies a large number of variables is measured and the identification of relevant variables influencing an outcome is an important task. For variable selection several procedures are available. However, focusing on one model only neglects that there usually exist other equally appropriate models. Bayesian or frequentist model averaging approaches have been proposed to improve the development of a predictor. With a larger number of variables (say more than ten variables) the resulting class of models can be very large. For Bayesian model averaging Occam’s window is a popular approach to reduce the model space. As this approach may not eliminate any variables, a variable screening step was proposed for a frequentist model averaging procedure. Based on the results of selected models in bootstrap samples, variables are eliminated before deriving a model averaging predictor. As a simple alternative screening procedure backward elimination can be used. Through two examples and by means of simulation we investigate some properties of the screening step. In the simulation study we consider situations with fifteen and 25 variables, respectively, of which seven have an influence on the outcome. With the screening step most of the uninfluential variables will be eliminated, but also some variables with a weak effect. Variable screening leads to more applicable models without eliminating models, which are more strongly supported by the data. Furthermore, we give recommendations for important parameters of the screening step.     

Relationship Preferences Among Gay and Lesbian Online Daters: Individual and Contextual Influences

There is currently little knowledge about what gay men and lesbians seek in a romantic relationship. This study extends the literature on gay men and lesbians' partnership preferences by engaging in the first large‐scale empirical study of the long‐term dating intentions and monogamy beliefs of gay and lesbian online daters across 53 regions in 8 European countries (N = 24,598). Looking at profile and preference information, the authors examined both individual and contextual determinants in a series of multilevel logistic regression analyses. They show that lesbians give more importance to monogamy but show less interest in starting a long‐term relationship. The data also reveal the importance of life course aspects such as relationship history and presence of children. Finally, the authors empirically demonstrate that social tolerance and legal recognition of same‐sex unions are associated with higher long‐term dating intentions and stronger monogamy beliefs.