The minimum regularized covariance determinant estimator

Statistics and Computing - The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using the subset of given size with lowest sample covariance determinant. Its...     

Families' experience of a single session therapy framework in advanced allied health practice for children with neurodevelopmental and behavioural disabilities: A thematic analysis of qualitative data

Single session therapy (SST) has emerging evidence for facilitating meaningful therapeutic change in a small number of consults, rather than more traditional long-term therapies. This study aimed to explore and describe the parent/caregiver and child experience with advanced allied health practitioners (AAHP) using SST for children with developmental disabilities, by understanding key characteristics of the consultation that led to therapeutic change. Eligible families attending an AAHP outpatient clinic for children with developmental and behavioural concerns participated in one to four SST consultations around their identified areas of need. Semi-structured interviews were conducted with 38 parents and 12 children, from 37 different families. Thematic analysis was used to analyse interviews. Demographic information was gathered through parent interviews and by reviewing hospital records. Four key themes emerged relating to the characteristics of the consultation that led to a positive experience for families: (1) the art of mutual presence; (2) the value of time; (3) the power of understanding; and (4) the trust of action. These attributes were influenced by the overarching complexities of the AAHP, the parent's and child's interpersonal skills, and the organisational mechanisms influencing patient care. Parents and children reported clinical change during the consult through increased understanding, and being heard, valued, and respected for their individual decisions on how to manage their child's and family's needs. This study provides preliminary evidence that utilising SST for children and families with developmental and behavioural needs facilitates a positive experience valued by families. SST, with a focus on a family's individual identified areas of concern, was valued and effective when delivered by AAHP. This suggests that SST, when appropriately administered by experienced and trained health practitioners, is useful for families dealing with children with developmental and behavioural challenges.     

robROSE: A robust approach for dealing with imbalanced data in fraud detection

Statistical Methods & Applications - A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set....     

Outlyingness: Which variables contribute most?

Outlier detection is an inevitable step to most statistical data analyses. However, the mere detection of an outlying case does not always answer all scientific questions associated with that data point. Outlier detection techniques, classical and robust alike, will typically flag the entire case as outlying, or attribute a specific case weight to the entire case. In practice, particularly in high dimensional data, the outlier will most likely not be outlying along all of its variables, but just along a subset of them. If so, the scientific question why the case has been flagged as an outlier becomes of interest. In this article, a fast and efficient method is proposed to detect variables that contribute most to an outlier’s outlyingness. Thereby, it helps the analyst understand in which way an outlier lies out. The approach pursued in this work is to estimate the univariate direction of maximal outlyingness. It is shown that the problem of estimating that direction can be rewritten as the normed solution of a classical least squares regression problem. Identifying the subset of variables contributing most to outlyingness, can thus be achieved by estimating the associated least squares problem in a sparse manner. From a practical perspective, sparse partial least squares (SPLS) regression, preferably by the fast sparse NIPALS (SNIPLS) algorithm, is suggested to tackle that problem. The performed method is demonstrated to perform well both on simulated data and real life examples.