基于模糊多属性决策的社区体育服务公众满意度测评研究

社区体育服务的发展水平作为城市文明程度和居民生活质量提高的标志,愈来愈受到人们的关注,社区体育服务公众满意度也日益成为了研究的焦点.结合满意度理论和我国社区体育服务的特色,引入基于期望值的模糊多属性决策方法构建了社区体育服务居民满意度测评体系,进行社区体育服务的满意度测评研究.综合考虑开展社区体育服务公众满意度评价的实际情况,采用三角模糊数得到带有定量指标和定性指标的区间型评价矩阵,并以实证研究验证该方法的合理性,从而为科学评价社区体育服务质量及公众满意度开辟一条新途径.     

沉默权制度应予确立

沉默权是无罪推定的核心内容,是保障犯罪嫌疑人、被告人的正当权利,实现司法公正的重要体现。根据沉默权的起源、发展的历程,借鉴国际社会采用沉默权制度的经验,分析法学界对沉默权的不同观点和态度,结合我国的国情和司法实践,所得结论是应当在我国确立沉默权制度,以推进我国的法治建设,体现人权保障和人文关怀。同时,对沉默权制度不可照抄照搬,盲目采用,而应充分考虑我国的国情和司法实践,创立有中国特色的沉默权制度,从而构建社会主义和谐社会。     

房地产开发项目三阶段投资决策的实物期权模型研究

房地产开发项目一般包括土地所有权的获得、建筑和销售三个阶段,对于其投资决策,传统的NPV法无法正确地评价管理的柔性价值。文章引入实物期权理论,指出实物期权是对实物投资的选择权,对于房地产开发项目,当每一阶段完成后,即拥有了对后续阶段的投资期权。针对企业投资房地产开发项目的三阶段决策建立了相应的实物期权模型,并给出了每一阶段的期权价值以及最佳投资点的临界价格。     

Book Review

Affective forecasting refers to the process of predicting emotional reactions to future events. It plays an important role in decision making, but is also prone to errors, such as the ‘impact bias’: a tendency to overestimate the intensity of future reactions. The impact bias has been considered evolutionarily adaptive, as it performs a protective function in motivating people to avoid risky behaviour. Affective forecasting may be qualitatively different in a risk-taking population such as problem gamblers (PGs). In particular, PGs may fail to show the impact bias. This study was the first to examine affective forecasting in PGs. PGs (N = 25) and controls (N = 29) were asked to predict how they would feel after completing a guessing task. As hypothesized, controls exaggerated how bad they would feel after losing at the task, whereas PGs accurately predicted their reactions. Encouraging PGs to focus on anticipated emotions may be a novel target for treatment interventions.     

The Subjectivity of Fairness: Managerial Discretion and Work–Life Balance

We use organizational justice theory to examine how perceptions of fairness affect the decision‐making process of line managers. In‐depth interviews were conducted with 35 Irish managers to explore how managers make organizational allocation decisions in cases where it is impractical to offer work–life balance accommodations to all employees. The findings suggest that firstly, managers construct the ‘life’ aspect of work–life balance within a heteronormative framework, where the emphasis is on caregiving and most usually parenting. Secondly, managers actively use their decision‐making powers around both formal and informal work–life balance supports to minimize injustice within their departments. By bringing together ideas about organizational justice and managerial decision‐making, we indicate how managers determine fairness through a decision‐making process narrowed by embedded gender role beliefs. Implications for future research and practice are discussed.     

The general class of chain estimators for the ratio of two means using double sampling

A wider class of chain based estimators for the ratio of two means of a finite population has been proposed by using two auxiliary variables. Singh et al.(1994) is a particular case of this class.     

Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment

Standard methods of estimation for autoregressive models are known to be biased in finite samples, which has implications for estimation, hypothesis testing, confidence interval construction and forecasting. Three methods of bias reduction are considered here: first-order bias correction, FOBC, where the total bias is approximated by the O(T^-1) bias; bootstrapping; and recursive mean adjustment, RMA. In addition, we show how first-order bias correction is related to linear bias correction. The practically important case where the AR model includes an unknown linear trend is considered in detail. The fidelity of nominal to actual coverage of confidence intervals is also assessed. A simulation study covers the AR(1) model and a number of extensions based on the empirical AR(p) models fitted by Nelson & Plosser (1982). Overall, which method dominates depends on the criterion adopted: bootstrapping tends to be the best at reducing bias, recursive mean adjustment is best at reducing mean squared error, whilst FOBC does particularly well in maintaining the fidelity of confidence intervals.     

Risk of Error and the Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

The accuracy of a binary diagnostic test is usually measured in terms of its sensitivity and its specificity, or through positive and negative predictive values. Another way to describe the validity of a binary diagnostic test is the risk of error and the kappa coefficient of the risk of error. The risk of error is the average loss that is caused when incorrectly classifying a non-diseased or a diseased patient, and the kappa coefficient of the risk of error is a measure of the agreement between the diagnostic test and the gold standard. In the presence of partial verification of the disease, the disease status of some patients is unknown, and therefore the evaluation of a diagnostic test cannot be carried out through the traditional method. In this paper, we have deduced the maximum likelihood estimators and variances of the risk of error and of the kappa coefficient of the risk of error in the presence of partial verification of the disease. Simulation experiments have been carried out to study the effect of the verification probabilities on the coverage of the confidence interval of the kappa coefficient.     

An L1-type estimator of multivariate location and shape

The main goal of the paper is to specify a suitable multivariate multilevel model for polytomous responses with a non-ignorable missing data mechanism in order to determine the factors which influence the way of acquisition of the skills of the graduates and to evaluate the degree programmes on the basis of the adequacy of the skills they give to their graduates. The application is based on data gathered by a telephone survey conducted, about two years after the degree, on the graduates of year 2000 of the University of Florence. A multilevel multinomial logit model for the response of interest is fitted simultaneously with a multilevel logit model for the selection mechanism by means of maximum likelihood with adaptive Gaussian quadrature. In the application the multilevel structure has a crucial role, while selection bias results negligible. The analysis of the empirical Bayes residuals allows to detect some extreme degree programmes to be further inspected.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada