Does Data Splitting Improve Prediction?

Data splitting divides data into two parts. One part is reserved for model selection. In some applications, the second part is used for model validation but we use this part for estimating the parameters of the chosen model. We focus on the problem of constructing reliable predictive distributions for future observed values. We judge the predictive performance using log scoring. We compare the full data strategy with the data splitting strategy for prediction. We show how the full data score can be decomposed into model selection, parameter estimation and data reuse costs. Data splitting is preferred when data reuse costs are high. We investigate the relative performance of the strategies in four simulation scenarios. We introduce a hybrid estimator called SAFE that uses one part for model selection but both parts for estimation. We discuss the choice to use a split data analysis versus a full data analysis

The Exact and Asymptotic Distributions of Cramérâ Von Mises Statistics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146839/1/rssb02077.pd

Time series forecasting with neural networks: a comparative study using the air line data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73285/1/1467-9876.00109.pd

Modeling continuous shape change for facial animation

The movement of landmarks on the human face can be recorded in 3D using motion capture equipment. We describe methods for the analysis of data collected on groups of subjects with a view to describing and assessing the differences between the facial motions of those groups. We focus on the smile motion in particular. The methods presented can be used more generally for continuous shape change data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47977/1/11222_2004_Article_5273891.pd

Modelling three-dimensional trajectories by using BÉzier curves with application to hand motion

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72561/1/j.1467-9876.2007.00592.x.pd

Trajectories of depression and generalised anxiety symptoms over the course of cognitive behaviour therapy in primary care: an observational, retrospective cohort

Background:Cognitive-behavioural therapy (CBT) has been shown to be an effective treatment for depression and anxiety. However, most research has focused on the sum scores of symptoms. Relatively little is known about how individual symptoms respond.Methods:Longitudinal models were used to explore how depression and generalised anxiety symptoms behave over the course of CBT in a retrospective, observational cohort of patients from primary care settings (n = 5306). Logistic mixed models were used to examine the probability of being symptom-free across CBT appointments, using the 9-item Patient Health Questionnaire and the 7-item Generalised Anxiety Disorder scale as measures.Results:All symptoms improve across CBT treatment. The results suggest that low mood/hopelessness and guilt/worthlessness improved quickest relative to other depressive symptoms, with sleeping problems, appetite changes, and psychomotor retardation/agitation improving relatively slower. Uncontrollable worry and too much worry were the anxiety symptoms that improved fastest; irritability and restlessness improved the slowest.Conclusions:This research suggests there is a benefit to examining symptoms rather than sum scores alone. Investigations of symptoms provide the potential for precision psychiatry and may explain some of the heterogeneity observed in clinical outcomes when only sum scores are considered

Visual and Statistical Modeling of Facial Movement in Patients With Cleft Lip and Palate

To analyze and display facial movement data from noncleft subjects and from patients with cleft lip and palate by using a new dynamic approach. The hypothesis was that there are differences in facial movement between the patients with cleft lip and palate and the noncleft subjects