Longitudinal Data Analysis and Time Series Commons^™

Comparative Analysis Of Teacher Effects Parameters In Models Used For Assessing School Effectiveness: Value-Added Models & Persistence, Merlin J. Kamgue

Graduate Theses and Dissertations

Longitudinal measures for students have become increasingly popular to estimate the effects of individual teachers and schools. Value-added models are one of the approaches using longitudinal data to evaluate teachers and schools. In the value-added model (VAM) literature, many statistical approaches have been developed and used to estimate teacher or school effects on student learning. This study opted to use a Bayesian multivariate model for evaluating teacher effects. The generalized persistence models can handle longitudinal data, not vertically scaled, allowing for a below-par teacher’s effects correlation across test administrations. This study first generated longitudinal students’ test score data and used …

A Bayesian Framework For Estimating Seismic Wave Arrival Time, Hua Zhong

Graduate Theses and Dissertations

Because earthquakes have a large impact on human society, statistical methods for better studying earthquakes are required. One characteristic of earthquakes is the arrival time of seismic waves at a seismic signal sensor. Once we can estimate the earthquake arrival time accurately, the earthquake location can be triangulated, and assistance can be sent to that area correctly. This study presents a Bayesian framework to predict the arrival time of seismic waves with associated uncertainty. We use a change point framework to model the different conditions before and after the seismic wave arrives. To evaluate the performance of the model, we …

Analysis Of Break-Points In Financial Time Series, Jean Remy Habimana

Graduate Theses and Dissertations

A time series is a set of random values collected at equal time intervals; this randomness makes these types of series not easy to predict because the structure of the series may change at any time. As discussed in previous research, the structure of time series may change at any time due to the change in mean and/or variance of the series. Consequently, based on this structure, it is wise not to assume that these series are stationary. This paper, discusses, a method of analyzing time series by considering the entire series non-stationary, assuming there is random change in unconditional …

Online Detection Of Outliers And Structural Breaks Using Sequential Monte Carlo Methods, Richard Wanjohi

Graduate Theses and Dissertations

Outliers and structural breaks occur quite frequently in time series data. Whereas outliers often contain valuable information

about the process under study, they are known to have serious negative impact on statistical data analysis. Most obvious effect is model misspecification and biased parameter estimation which results in wrong conclusions and inaccurate predictions. Structural time series consist of underlying features such as level, slope, cycles or seasonal components. Structural breaks are permanent disruptions of one or more of these components and might be a signal of serious changes in the observed process.

Detecting outliers and estimating the location of structural breaks …