Does the β-measure make empirical sense? A vignette experiment on humans’ judgment of social dominance

Dominance hierarchies play an important role in governing the social interactions of humans and other species of social animals. In a social group, dominance relations can be inferred from the directed network of matchups between actors. Methodologists have proposed different ways to measure social dominance in directed networks. One such measure, the “β-measure” (van den Brink and Gilles, 2000), emphasizes the quality of defeated opponents in a way that an actor is seen as being more dominant when s/he defeats opponents who are more rarely defeated. While insightful in theory, the validity of the measure in people’s perception remains questionable, considering the cognitive complexity imposed by this measure, compared to a simpler measure that merely counts the number of defeated opponents. We conducted a vignette experiment with human subjects (professional athletes) to test their judgments of the dominance relation in a hypothetical tournament. Fitting our parametric model to peoples’ evaluations in the experiment, we found strong evidence in support of the β-measure: Although, in general, contestants who win more in the tournament are regarded as being more dominant, the contents of the winning records matter, such that those who beat more victorious opponents are further regarded as more dominant than those who defeat less victorious opponents. We also found a gender difference, in that men have a stronger propensity than women to adopt the β-measure when judging social dominance.     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

Smoothed bootstrap bandwidth selection for nonparametric hazard rate estimation

A smoothed bootstrap method is presented for the purpose of bandwidth selection in nonparametric hazard rate estimation for iid data. In this context, two new bootstrap bandwidth selectors are established based on the exact expression of the bootstrap version of the mean integrated squared error of some approximations of the kernel hazard rate estimator. This is very useful since Monte Carlo approximation is no longer needed for the implementation of the two bootstrap selectors. A simulation study is carried out in order to show the empirical performance of the new bootstrap bandwidths and to compare them with other existing selectors. The methods are illustrated by applying them to a diabetes data set.     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

Parametrizing the Kepler exoplanet period-radius distribution with the bivariate normal inverse Gaussian distribution

This paper presents a simple and robust method for obtaining a comprehensive understanding of the joint period and radius distribution in Kepler exoplanets. The proposed method is based on particle swarm optimization and bivariate Normal Inverse Gaussian distribution. Furthermore, in the construction of the probability density function, this study selects planet-host stars with the GK-type. The injecting approach is also employed to solve the survey completeness of sample. The resulting occurrence rate of Earth analogs is 0.025 with a 95% bootstrap confidence interval between 0.023 and 0.032.     

Conditional design of the EWMA median chart with estimated parameters

The exponentially weighted moving average (EWMA) chart is often designed assuming the process parameters are known. In practice, the parameters are rarely known and need to be estimated from Phase I samples. Different Phase I samples are used when practitioners construct their own control chart's limits, which leads to the “Phase I between-practitioners” variability in the in-control average run length (ARL) of control charts. The standard deviation of the ARL (SDARL) is a good alternative to quantify this variability in control charts. Based on the SDARL metric, the performance of the EWMA median chart with estimated parameters is investigated in this paper. Some recommendations are given based on the SDARL metric. The results show that the EWMA median chart requires a much larger amount of Phase I data in order to reduce the variation in the in-control ARL up to a reasonable level. Due to the limitation of the amount of the Phase I data, the suggested EWMA median chart is designed with the bootstrap method which provides a good balance between the in-control and out-of-control ARL values.     

A comparative study of confidence intervals to assess biosimilarity from analytical data

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods.     

The impact of the European Youth Guarantee on active labour market policies: A convergence analysis

The European Union’s (EU) Youth Guarantee aims to improve the labour market situation of young people. Rather than prescribing a uniform policy model, it acknowledges that supportive measures need to align with national, regional and local circumstances. It thus seeks to promote mutual policy learning through the open method of coordination. As an innovative measure, the EU has deployed funding programmes to support the domestic measures related to the Youth Guarantee. We therefore examined in this study whether this mix of recommendations and financial incentives has entailed a convergence of member state policies. Our analysis of policy outputs for the period 2007?2014 yields a mixed empirical picture. There is catching‐up convergence regarding policies’ sectoral coverage but increasing divergence concerning the number of adopted policy instruments. The first two years of financial incentives did not produce any effect on enhancing policy experimentation among less active member states. We offer an optimistic and a pessimistic interpretation of these findings.     

一种考虑风险的项目活动持续时间的估算方法

在介绍PERT网络模型基础上 ,从影响项目工期的风险因素出发 ,分析风险因素对项目活动的影响 ,提出了一种新的项目活动持续时间的估算方法 ,通过多次模拟确定项目持续时间分布和期望工期 ,并可找出影响项目工期最大的因素。