After defining the structure to a signal detection scheme, this dissertation describes and addresses some of the unresolved issues associated with its use when the interference encountered is impulsive. The alpha-stable (alpha-S) family of distributions is used as a model of this interference due to its physical interpretation and its general form. Despite its attractive features, difficulties arise in using this distribution due to, amongst other things, the lack of a general closed form expression for its probability density function. Relevant to the detection scheme used, this affects parameter estimation, signal detector design and goodness-of-fit tests. Significant contributions are made in the latter through the introduction of characteristic function based test that uses the parametric bootstrap. A modification of this test is then made to define a test of the level of impulsive behaviour - again the parametric bootstrap is employed to maintain levels of significance for this and another test based on testing the alpha-S parameter values. The performance of these tests is examined under simulated and two sources of real, impulsive data, namely human heart rate variability and fluctuations in stock prices. Once the appropriateness of the model assumption has been verified, the final, signal detection process may take place. Detectors based on the locally optimum criterion and approximations to it are described and compared to their rank-based counterparts. Results are presented that suggest compelling arguments based on performance and computational complexity for the consideration of rank-based techniques.Keywords: Impulsive behaviour, alpha-stable distribution, stable laws, Gaussianity testing, parameter estimation, goodness-of-fit, parametric bootstrap, signal detection, locally optimum detectors, rank-based detectors.

Purpose: The systematic review summarised the research investigating potential risk factors for impulsive behaviours in forensic populations. The empirical study examined the predictive utility of clinician rated, self-report and behavioural measures of impulsivity in detecting violence and antisocial behaviour in forensic mental health inpatient settings. Method: The review is comprised of 9 studies identified through electronic database searches using a structured search strategy and predetermined inclusion criteria. The empirical study employed a cross-sectional design using retrospective and prospective statistical analysis. Forty-three participants were recruited from secure forensic mental health inpatient settings across Scotland and data collected from clinician rated, self-report and behavioural measures of impulsivity. Results: The review found original evidence to suggest that traumatic brain injury, substance and alcohol misuse, trauma and sleep as possible predictors of impulsive behaviour in forensic populations. The empirical study found a relatively consistent relationship between impulsive behaviour and violent or antisocial behaviour in a sample of forensic mental health inpatients. Conclusions: The systematic review identified a limited number of risk factors thought to influence impulsive behaviour in forensic populations. The review highlights the need for future research with improved methodological design to further explore contributory factors for increased levels of impulsivity. Findings from the empirical study reveal clinician rating of impulsive behaviour to be the most sensitive in predicting future incidents of violent and antisocial behaviour, which may be supplemented by the addition of a self-report measure.