Mixtures of Common Skew-t Factor Analyzers

A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data. By assuming common component factor loadings, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well-modelled by the mixtures of factor analyzers model or a variant thereof. Furthermore, assuming that the component densities follow a skew-t distribution allows robust clustering of skewed data. The alternating expectation-conditional maximization algorithm is employed for parameter estimation. We demonstrate excellent clustering performance when our model is applied to real and simulated data.This paper marks the first time that skewed common factors have been used

Mixtures of Skew-t Factor Analyzers

In this paper, we introduce a mixture of skew-t factor analyzers as well as a family of mixture models based thereon. The mixture of skew-t distributions model that we use arises as a limiting case of the mixture of generalized hyperbolic distributions. Like their Gaussian and t-distribution analogues, our mixture of skew-t factor analyzers are very well-suited to the model-based clustering of high-dimensional data. Imposing constraints on components of the decomposed covariance parameter results in the development of eight flexible models. The alternating expectation-conditional maximization algorithm is used for model parameter estimation and the Bayesian information criterion is used for model selection. The models are applied to both real and simulated data, giving superior clustering results compared to a well-established family of Gaussian mixture models

Parsimonious Shifted Asymmetric Laplace Mixtures

A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An explicit two-stage parameter estimation procedure is described, and the Bayesian information criterion and the integrated completed likelihood are compared for model selection. This novel family of models is applied to real data, where it is compared to its Gaussian analogue within clustering and classification paradigms

Protocol for the development and validation procedure of the managing the link and strengthening transition from child to adult mental health care (MILESTONE) suite of measures

Background: Mental health disorders in the child and adolescent population are a pressing public health concern. Despite the high prevalence of psychopathology in this vulnerable population, the transition from Child and Adolescent Mental Health Services (CAMHS) to Adult Mental Health Services (AMHS) has many obstacles such as deficiencies in planning, organisational readiness and policy gaps. All these factors contribute to an inadequate and suboptimal transition process. A suite of measures is required that would allow young people to be assessed in a structured and standardised way to determine the on-going need for care and to improve communication across clinicians at CAMHS and AMHS. This will have the potential to reduce the overall health economic burden and could also improve the quality of life for patients travelling across the transition boundary. The MILESTONE (Managing the Link and Strengthening Transition from Child to Adult Mental Health Care) project aims to address the significant socioeconomic and societal challenge related to the transition process. This protocol paper describes the development of two MILESTONE transition-related measures: The Transition Readiness and Appropriateness Measure (TRAM), designed to be a decision-making aide for clinicians, and the Transition Related Outcome Measure (TROM), for examining the outcome of transition. Methods: The TRAM and TROM have been developed and were validated following the US FDA Guidance for Patient-reported Outcome Measures which follows an incremental stepwise framework. The study gathers information from service users, parents, families and mental health care professionals who have experience working with young people undergoing the transition process from eight European countries. Discussion: There is an urgent need for comprehensive measures that can assess transition across the CAMHS/AMHS boundary. This study protocol describes the process of development of two new transition measures: the TRAM and TROM. The TRAM has the potential to nurture better transitions as the findings can be summarised and provided to clinicians as a clinician-decision making support tool for identifying cases who need to transition and the TROM can be used to examine the outcomes of the transition process. Trial registration: MILESTONE study registration: ISRCTN83240263 Registered 23-July-2015 - ClinicalTrials.gov NCT03013595 Registered 6 January 2017