Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Att tillämpa återkommande mätning av kundnöjdhet i IT-projekt : En vidareutvecklad modell baserad på modellerna American Customer Satisfaction Index (ACSI) och SERVQUAL

I dagens samhälle är det allt viktigare för företag att behålla sina existerande kunder då konkurrensen blir allt hårdare. Detta medför att företag försöker vidta åtgärder för att vårda relationer med sina kunder. Detta problem är även högst relevant inom IT-branschen. Inom IT-branschen är det vanligt att arbeta agilt i IT-projekt. Vår samarbetspartner har sett ett ökat behov av att mäta servicekvalitet på ett återkommande sätt inom IT-projekt, detta för att mäta relevanta variabler som sträcker sig utanför kravspecifikationen. För att mäta framgång gällande detta arbetssätt vill man kunna mäta Nöjd Kund Index (NKI) för att kunna jämföra IT-projekt internt i företaget. Då tidigare forskning visat avsaknad av modeller innehållande både mätning av servicekvalitet samt NKI har lämplig litteratur studerats där det framkommit att modellen SERVQUAL är vedertagen för mätning av servicekvalitet och modellen American Customer Satisfaction Index (ACSI) är vedertagen för mätning av NKI. Detta har legat till grund för arbetets problemformulering och syfte. Syftet med arbetet är att skapa en vidareutvecklad modell för mätning av NKI för att jämföra IT-projekt internt samt återkommande mätning av servicekvalitet inom IT-projekt. Framtagande av denna modell har sedan skett genom forskningsstrategin Design and Creation. Intervjuer har genomförts för kravfångst till den vidareutvecklade modellen. Resultatet av denna forskningsstrategi blev sedan en vidareutvecklad modell baserad på ovan nämnda modeller med återkommande förhållningssätt för mätning av servicekvalitet inom IT-projekt och mätning av NKI för att jämföra IT-projekt internt i företaget. Den framtagna modellen har sedan verifierats genom ytterligare intervjuer med respondenter som innehar god erfarenhet från kundsidan av IT-projekt. Från dessa intervjuer kunde sedan slutsats dras att denna modell är att anse som applicerbar i empirin gällande IT-projekt.