Outlier detection in high-dimensional regression model

An outlier is defined as an observation that is significantly different from the others in its dataset. In high-dimensional regression analysis, datasets often contain a portion of outliers. It is important to identify and eliminate the outliers for fitting a model to a dataset. In this paper, a novel outlier detection method is proposed for high-dimensional regression problems. The leave-one-out idea is utilized to construct a novel outlier detection measure based on distance correlation, and then an outlier detection procedure is proposed. The proposed method enjoys several advantages. First, the outlier detection measure can be simply calculated, and the detection procedure works efficiently even for high-dimensional regression data. Moreover, it can deal with a general regression, which does not require specification of a linear regression model. Finally, simulation studies show that the proposed method behaves well for detecting outliers in high-dimensional regression model and performs better than some other competing methods.     

Do individuals successfully cover up their lies? Evidence from a compliance experiment

We study how well individuals in a face-to-face situation can delude others. We exploit data from a laboratory experiment in which participants were asked to assess video-taped statements as being rather truthful or untruthful. The statements are face-to-face tax declarations. The video clips feature each subject twice making the same declaration: One time the subject is reporting honestly, and the other time willingly dishonestly. This allows us to investigate within-subject differences in perceived truthfulness. Our study provides several novel insights. We find that individuals can cover up their lies successfully. On average, a subject is perceived as more truthful if she deceives than if she reports honestly. In particular, individuals with a genuine dishonest appearance manage to increase their perceived truthfulness by up to 14 percent when lying. Moreover, our results show that a subject appears less truthful if she had previously been caught lying. Being detected as a liar previously appears to impair self-confidence and to thereby lower an individual’s ability to deceive.     

Investment communities: Behavioral attitudes and economic dynamics

Using a real-world data set encompassing the daily portfolio holdings and exposures of complex investment funds, we derive a set of quantitative attributes to capture essential behavioral features of fund managers. We find the existence and stability of three investment attitudes, namely the conservative, the reactive, and the pro-active profiles, defining communities that respond differently when facing external shocks. The conservative community has behavioral similarities that tend to decrease due to external shocks, the reactive community members greatly increase their activity level especially during turmoil phases, while delegated investors in the pro-active community are more resilient to turbulence and counterbalance the impact of the events by adjusting their portfolio exposures in advance. We show that exogenous shocks only temporarily perturb the behavioral traits of the communities which then go back to their original states once the distress is embedded.     

Exploring the essence of enabling child participation within child protection services

The chances that children have to participate in child protection services are largely contingent upon the attitudes and skills of case managers. They have a crucial role in ensuring that a child's voice is being listened to and acknowledged in often sensitive dialogues. Therefore, the aim of this study is to investigate what case managers need to facilitate child participation. By handing case managers a participation toolbox developed by children and through the use of reflexive monitoring in action, we elaborated on the barriers faced in practice. The results show that the intention to facilitate child participation is quickly overruled by often subconscious thoughts and beliefs, stemming from the complex interplay of child image, protection, and participation. Case managers need to be aware of this interplay and how this unknowingly steers their actions. Therefore, iterative reflection should be facilitated within organizations for an actual change in their daily practice and to amplify the role of children in the decision‐making processes within child protection services.     

Serious case reviews: The lived experience of Black children

Despite the many high‐profile Black child deaths in England, race as a factor remains a largely underexplored factor of serious case reviews (SCRs). Evidence from analysis of SCRs indicates that race receives limited attention, or is virtually absent. Given that the main function of SCRs is to provide opportunities for learning lessons to improve practice, the way in which issues of race and culture may influence child protection processes for Black children is therefore of critical importance. In this article, we employ content analysis to examine the extent that race and cultural factors are considered in SCRs involving Black children. It is argued that race is often an important factor influencing Black children's experiences of abuse and neglect, as well as their encounters in the child protection system. This article therefore poses two key questions: (a) What questions are asked about race, ethnicity, and culture in SCRs concerning Black children? (b) How did the SCRs extract lessons to be learnt for improving practice to safeguard Black children? By extending the analysis of race and ethnicity in SCRs, this article furthers our understandings of the needs of Black children in the child protection system.     

Resource Distribution in Multiple Attacks with Imperfect Detection of the Attack Outcome

This article extends the previous research of consecutive attacks strategy by assuming that an attacker observes the outcome of each attack imperfectly. With given probabilities it may wrongly identify a destroyed target as undestroyed, and wrongly identify an undestroyed target as destroyed. The outcome of each attack is determined by a contest success function that depends on the amount of resources allocated by the defender and the attacker to each attack. The article suggests a probabilistic model of the multiple attacks and analyzes how the target destruction probability and the attacker's relative resource expenditure are impacted by the two probabilities of incorrect observation, the attacker's and defender's resource ratio, the contest intensity, the number of attacks, and the resource distribution across attacks. We analyze how the attacker chooses the number of attacks, the attack stopping rule, and the optimal resource distribution across attacks to maximize its utility.     

Mean shift and influence measures in linear measurement error models with stochastic linear restrictions

We present influence diagnostics for linear measurement error models with stochastic linear restrictions using the corrected likelihood of Nakamura in 1990. The case deletion and mean shift outlier models are developed to identify outlying and influential observations. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. The analogs of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion models. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to evaluate the performance of the proposed estimators based on the mean squares error criterion and the score test statistic. Finally, a numerical example is given to illustrate the theoretical results.     

Composite Estimation for Single‐Index Models with Responses Subject to Detection Limits

We propose a semiparametric estimator for single‐index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the non‐parametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors but much more efficient for heavy‐tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.     

Supportive Housing for Addicted,Incarcerated Homeless Adults

ABSTRACT

Homeless populations often suffer from mental illness, substance abuse, and criminality. Interventions, such as supportive housing, can have positive impacts, although benefits can be limited. This study examined outcomes for clients (N = 102) of a new supportive housing intervention. Use of formal treatment, jail contact, and community stability were compared pre- and post-housing. Jail bookings and residential substance abuse treatment significantly declined post-housing, while clients improved in income level, access to food, and housing stability. Results from official justice and treatment system data suggest that supportive housing can lead to significant changes. Future research is necessary to understand the relationship between client characteristics and outcomes.     

Effective eProcurement implementation process

As eProcurement is gaining popularity in business practice, researchers as well as practitioners have addressed the need of defining a proper eProcurement implementation strategy. This article aims to build an eProcurement implementation framework, which considers the main variables that have an impact on the eProcurement success and the links among them. The goal is to identify the main decisions that companies should take during the three principal phases of the eProcurement adoption (pre-implementation; implementation and post-implementation). Six case studies at different levels of eProcurement adoption have been used to identify the peculiarities of each phase of the process and to provide contingent guidelines.