Bayesian influence diagnostics in radiocarbon dating

Linear models constitute the primary statistical technique for any experimental science. A major topic in this area is the detection of influential subsets of data, that is, of observations that are influential in terms of their effect on the estimation of parameters in linear regression or of the total population parameters. Numerous studies exist on radiocarbon dating which propose a value consensus and remove possible outliers after the corresponding testing. An influence analysis for the value consensus from a Bayesian perspective is developed in this article.     

A Practical Implementation and Bayesian Estimation in Franklin's Randomized Response Procedure

A Bayesian estimator based on Franklin's randomized response procedure is proposed for proportion estimation in surveys dealing with a sensitive character. The method is simple to implement and avoids the usual drawbacks of Franklin's estimator, i.e., the occurrence of negative estimates when the population proportion is small. A simulation study is considered in order to assess the performance of the proposed estimator as well as the corresponding credible interval.     

Change-Point Estimation in Long Memory Nonparametric Models with Applications

We consider the estimation of a change point or discontinuity in a regression function for random design model with long memory errors. We provide several change-point estimators and investigate the consistency of the estimators. Using the fractional ARIMA process as an example of long memory process, we report a small Monte Carlo experiment to compare the performance of the estimators in finite samples. We finish by applying the method to a climatological data example.     

Accuracy Analysis of the Percentile Method for Estimating Non Normal Manufacturing Quality

We consider asymmetric kernel estimates based on grouped data. We propose an iterated scheme for constructing such an estimator and apply an iterated smoothed bootstrap approach for bandwidth selection. We compare our approach with competing methods in estimating actuarial loss models using both simulations and data studies. The simulation results show that with this new method, the estimated density from grouped data matches the true density more closely than with competing approaches.     

Information search,coherence effects,and their interplay in legal decision making

Legal judgments require one to make sense of a complex set of typically contradicting pieces of information that can easily be interpreted in a biased manner. This systematic judgment bias can be caused by biased information search (i.e., confirmatory search) as well as biased information processing (i.e., coherence effects) in which the interpretation of information is changed to fit the emerging favored option. In four studies, we investigate the complex interplay between both kinds of influences. Participants completed three legal cases in which they could freely search for information. We manipulated between subjects whether systematic search was possible or not and measured the assessment of each selected piece of information. In line with previous studies, we observe strong coherence effects in each study, in that the evidence interpretation strongly depended on the current tendency towards acquittal or conviction. In contrast to our expectation, however, people searched for information that was contrary to their current belief in the given case (i.e., disconfirmatory information search). We also observed a trend towards an interaction between both factors, in that coherence effects were slightly stronger for neutral and pro-guilty evidence when systematic information search was possible. Our results underline an unconscious striving for coherence when making complex judgments that is not easily corrected.     

Compensation or competition: Bias in immigrants' access to active labour market measures

Whether participation in active labour market programmes (ALMPs) pushes individuals back into employment depends on the programme's characteristics. On the basis of encompassing registry data that allow us to control for usually unobserved employability, we find evidence of a systematic access bias whereby jobcentre caseworkers in Switzerland assign unemployed persons to activation measures based on a competition logic that is mainly driven by an economic rationale and the jobcentre's performance evaluation. This practice seems problematic because it results in an overrepresentation of immigrants in measures with little efficacy rather than in measures that could compensate for employability disadvantages. Conversely, Swiss citizens are more likely to enter beneficial human-capital-intensive measures. It is plausible that this discrepancy in programme participation amplifies the general labour market disadvantages of immigrants.     

Copula‐based semiparametric analysis for time series data with detection limits

The analysis of time series data with detection limits is challenging due to the high‐dimensional integral involved in the likelihood. Existing methods are either computationally demanding or rely on restrictive parametric distributional assumptions. We propose a semiparametric approach, where the temporal dependence is captured by parametric copula, while the marginal distribution is estimated non‐parametrically. Utilizing the properties of copulas, we develop a new copula‐based sequential sampling algorithm, which provides a convenient way to calculate the censored likelihood. Even without full parametric distributional assumptions, the proposed method still allows us to efficiently compute the conditional quantiles of the censored response at a future time point, and thus construct both point and interval predictions. We establish the asymptotic properties of the proposed pseudo maximum likelihood estimator, and demonstrate through simulation and the analysis of a water quality data that the proposed method is more flexible and leads to more accurate predictions than Gaussian‐based methods for non‐normal data. The Canadian Journal of Statistics 47: 438–454; 2019 © 2019 Statistical Society of Canada     

Parimutuel betting on the eSports duels: Evidence of the reverse favourite-longshot bias

We analyse betting behaviour patterns of the visitors of the specialized betting website dedicated to the popular eSports game Counter-Strike: Global Offensive. The reverse favourite-longshot bias is found, which is rather unusual for parimutuel betting markets, where favourite-longshot bias is more common. We define simple betting strategies based on the bets on underdogs and show that these strategies make a sufficiently large positive profit both on the in-sample and out-of-sample datasets, which is a sign of market inefficiency. In matches with popular underdogs, the sentiment bias decreases the market inefficiency caused by the reverse favourite-longshot bias. At the same time, the role of the geographical location of teams in the reverse favourite-longshot bias is only marginal.     

从偏见视角解读医患关系

偏见是个体以不正确或不充分的信息为依据,并对其他人形成先入为主的负性判断,泛化至群体中则会表现为人们对某群体产生片面或错误的认知。文章从消极刻板印象、期望差异与归因偏差这三个角度解释了医患群体产生偏见的心理机制,在此基础上讨论偏见对医患关系造成的消极影响,并结合社会现实提出减少医患双方偏见的方法。     

PCA likelihood ratio test approach for attributed social networks monitoring

Abstract

One of the most important factors in building and changing communication mechanisms in social networks is considering features of the members of social networks. Most of the existing methods in network monitoring don’t consider effects of features in network formation mechanisms and others don’t lead to reliable results when the features abound or when there are correlations among them. In this article, we combined two methods principal component analysis (PCA) and likelihood method to monitor the underlying network model when the features of individuals abound and when some of them have high correlations with each other.