Bayesian lasso binary quantile regression

In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Existing approaches to variable selection in a binary classification context are sensitive to outliers, heteroskedasticity or other anomalies of the latent response. The method proposed in this study overcomes these problems in an attractive and straightforward way. A Laplace likelihood and Laplace priors for the regression parameters are proposed and estimated with Bayesian Markov Chain Monte Carlo. The resulting model is equivalent to the frequentist lasso procedure. A conceptional result is that by doing so, the binary regression model is moved from a Gaussian to a full Laplacian framework without sacrificing much computational efficiency. In addition, an efficient Gibbs sampler to estimate the model parameters is proposed that is superior to the Metropolis algorithm that is used in previous studies on Bayesian binary quantile regression. Both the simulation studies and the real data analysis indicate that the proposed method performs well in comparison to the other methods. Moreover, as the base model is binary quantile regression, a much more detailed insight in the effects of the covariates is provided by the approach. An implementation of the lasso procedure for binary quantile regression models is available in the R-package bayesQR

Late adoptions:Attachment security and emotional availability in mother-child and father-child dyads

A growing body of research suggests that a history of neglect, abuse and institutionalization can negatively affect late-adopted children's attachment representations, and that adoptive parents can play a key role in enabling adopted children to earn secure attachments. Still, only a few studies have explored the quality of caregiver-child interaction in adoptive families. The present study aimed at verifying both the concordance of attachment in adoptive dyads (mother-children and father-children) and the relationship between attachment representations and parent-child interaction. The research involved 20 adoptive families in which the child's arrival had occurred between 12 to 36 months before the assessment, and where children were aged between 4.5 and 8.5 years. Attachment was assessed through the Adult Attachment Interview for parents and through the Manchester Child Attachment Story Task for children. The emotional quality of parent-child interaction was assessed trough the Emotional Availability Scales. Our results pointed out the presence of a relation between attachment representations of late-adopted children and their adoptive mothers (75%, K = 0.50, p =.025). In addition, we found that both insecure children and mothers showed lower levels of EA than secure ones. Some explanations are presented about why, in the early post-adoption period, child attachment patterns and dyadic emotional availability seem to be arranged on different frameworks for the two parental figures

From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour

One-class classification is the standard procedure for novelty detection. Novelty detection aims to identify observations that deviate from a determined normal behaviour. Only instances of one class are known, whereas so called novelties are unlabelled. Traditional novelty detection applies methods from the field of outlier detection. These standard one-class classification approaches have limited performance in many real business cases. The traditional techniques are mainly developed for industrial problems such as machine condition monitoring. When applying these to human behaviour, the performance drops significantly. This paper proposes a method that improves existing approaches by creating semi-synthetic novelties in order to have labelled data for the two classes. Expert knowledge is incorporated in the initial phase of this data generation process. The method was deployed on a real-life test case where the goal was to detect fraudulent subscriptions to a telecom family plan. This research demonstrates that the two-class expert model outperforms a one-class model on the semi-synthetic dataset. In a next step the model was validated on a real dataset. A fraud detection team of the company manually checked the top predicted novelties. The results show that incorporating expert knowledge to transform a one-class problem into a two-class problem is a valuable method

Social ties in customer referral programs

Customer referral programs are marketing programs in which existing customers are rewarded for bringing in new customers. The aim is to attract new customers by leveraging the social connections of existing customers with potential customers. Previous research has shown that referred customers are more valuable to a firm than non-referred customers. However, previous research solely focused on the customer lifetimevalueofthenewlyreferredcustomersanddoesnotlookatthe social network characteristics. A study by Kumar et al. (2010) argues that we shouldconsider two parts of customer value, namelycustomer lifetimevalueandcustomerreferralvalue. Thelattercanbeconceived as a customer’s potential to grow the network through referrals. Early work by Granovetter (1973) highlights the importance of weak social connections, like acquaintances, in a network due to their position as bridges, connecting different communities. Extending this knowledge to customer referral programs, we can argue that referrals over weak links are powerful for accessing new communities. In this study, we investigatetheeffectofreferralsandthetiestrengthbetweentheexistingandpotentialcustomerontheresultinggrowthofthenetwork. The finding of this study are particularly useful for start-ups or marketing campaigns aiming to grow the customer base

From one-class to two-class classification by incorporating expert knowledge

In certain business cases the aim is to identify observations that deviate from an identified normal behaviour. It is often the case that only instances of the normal class are known, whereas so called novelties are undiscovered. Novelty detection or anomaly detection approaches usually apply methods from the field of outlier detection. However, anomalies are not always outliers and outliers are not always anomalies. The standard one-class classification approaches therefore underperform in many real business cases. Drawing upon literature about incorporating expert knowledge,we come up with a new method that significantly improves the predictive performance of a one-class model. Combining the available data and expert knowledge about potential anomalies enables us to create synthetic novelties. The latter are incorporated into a standard two-class predictive model. Based on a telco dataset, we prove that our synthetic two-class model clearly outperforms a standard one-class model on the synthetic dataset. In a next step the model was applied to real data. Top identified novelties were manually checked by experts. The results indicate that incorporating expert knowledge to transform a one-class problem into a two-class problem is a valuable method