Longitudinal Data Analysis and Time Series Commons^™

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 …

Spatiotemporal Crime Analysis, James Q. Tay, Abish Malik, Sherry Towers, David Ebert

The Summer Undergraduate Research Fellowship (SURF) Symposium

There has been a rise in the use of visual analytic techniques to create interactive predictive environments in a range of different applications. These tools help the user sift through massive amounts of data, presenting most useful results in a visual context and enabling the person to rapidly form proactive strategies. In this paper, we present one such visual analytic environment that uses historical crime data to predict future occurrences of crimes, both geographically and temporally. Due to the complexity of this analysis, it is necessary to find an appropriate statistical method for correlative analysis of spatiotemporal data, as well …

Genetic Predictors Of Metabolic Side Effects Of Diuretic Therapy, Jorge L. Del Aguila

Dissertations and Theses (Open Access)

Thiazide diuretics are a recommended first-line monotherapy for hypertension (i.e.SBP>140 mmHg or DBP>90 mmHg). Even so, diuretics are associated with adverse metabolic side effects, such as hyperlipidemia, hyperglycemia and hypokalemia which increase the risk of developing type II diabetes. This thesis used three analytical strategies to identify and quantify genetic factors that contribute to the development of adverse metabolic effects due to thiazide diuretic treatment. I performed a genome-wide association study (GWAS) and meta-analysis of the change in fasting plasma glucose and triglycerides in response to HCTZ from two different clinical trials: the Pharmacogenomic Evaluation of Antihypertensive Responses …

A Stochastic Parameter Regression Model For Long Memory Time Series, Rose Marie Ocker

Boise State University Theses and Dissertations

In a complex and dynamic world, the assumption that relationships in a system remain constant is not necessarily a well-founded one. Allowing for time-varying parameters in a regression model has become a popular technique, but the best way to estimate the parameters of the time-varying model is still in discussion. These parameters can be autocorrelated with their past for a long time (long memory), but most of the existing models for parameters are of the short memory type, leaving the error process to account for any long memory behavior in the response variable. As an alternative, we propose a long …

High Frequency Data: Modeling Durations Via The Acd And Log Acd Models, Lilian Cheung

Honors Scholar Theses

This thesis proposes a method of finding initial parameter estimates in the Log ACD₁ model for use in recursive estimation. The recursive estimating equations method is applied to the Log ACD₁ model to find recursive estimates for the unknown parameters in the model. A literature review is provided on the ACD and Log ACD models, and on the theory of estimating equations. Monte Carlo simulations indicate that the proposed method of finding initial parameter estimates is viable. The parameter estimation process is demonstrated by fitting an ACD model and a Log ACD model to a set of IBM …

Time Series Decomposition Using Singular Spectrum Analysis, Cheng Deng

Electronic Theses and Dissertations

Singular Spectrum Analysis (SSA) is a method for decomposing and forecasting time series that recently has had major developments but it is not yet routinely included in introductory time series courses. An international conference on the topic was held in Beijing in 2012. The basic SSA method decomposes a time series into trend, seasonal component and noise. However there are other more advanced extensions and applications of the method such as change-point detection or the treatment of multivariate time series. The purpose of this work is to understand the basic SSA method through its application to the monthly average sea …

Mediation Analysis With Time-Varying Exposures And Mediators, Tyler J. Vanderweele, Eric Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

In this paper we consider mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation, and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are no time-varying confounders affected by prior exposure and mediator values, identification of direct and indirect effects is achieved by a longitudinal version of Pearl's mediation formula. When there are time-varying confounders affected …

Set-Based Tests For Genetic Association In Longitudinal Studies, Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Ana V. Diez Roux, Bhramar Mukherjee

The University of Michigan Department of Biostatistics Working Paper Series

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set …

Testing Longitudinal Data By Logarithmic Quantiles, Manfred Denker, Lucia Tabacu

Mathematics & Statistics Faculty Publications

The shoulder tip pain study of Lumley [13] is re-investigated. It is shown that the new logarithmic quantile estimation (LQE) technique in [9] applies and behaves well under singular covariance structure and small sample sizes as in the shoulder tip pain study. The findings in [6] can be assured under weaker assumptions using a combination of LQE and an ANOVA type statistic. © 2014, Institute of Mathematical Statistics.

Natural Phenomena As Potential Influence On Social And Political Behavior: The Earth’S Magnetic Field, Jackie R. East

Theses and Dissertations--Political Science

Researchers use natural phenomena in a number of disciplines to help explain human behavioral outcomes. Research regarding the potential effects of magnetic fields on animal and human behavior indicates that fields could influence outcomes of interest to social scientists. Tests so far have been limited in scope. This work is a preliminary evaluation of whether the earth’s magnetic field influences human behavior it examines the baseline relationship exhibited between geomagnetic readings and a host of social and political outcomes. The emphasis on breadth of topical coverage in these statistical trials, rather than on depth of development for any one model, …

Modelling And Analysis On Noisy Financial Time Series, Jinsong Leng

Research outputs 2014 to 2021

Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular prob-lem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical …