Longitudinal Data Analysis and Time Series Commons^™

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 …

Repeat Sales House Price Index Methodology, Chaitra Nagaraja, Lawrence Brown, Susan Wachter

Chaitra H Nagaraja

No abstract provided.

Spectral Density Shrinkage For High-Dimensional Time Series, Mark Fiecas, Rainer Von Sachs

Mark Fiecas

Multi-State Models For Natural History Of Disease, Amy Laird, Rebecca A. Hubbard, Lurdes Y. T. Inoue

UW Biostatistics Working Paper Series

Longitudinal studies are a useful tool for investigating the course of chronic diseases. Many chronic diseases can be characterized by a set of health states. We can improve our understanding of the natural history of the disease by modeling the sequence of visited health states and the duration in each state. However, in most applications, subjects are observed only intermittently. This observation scheme creates a major modeling challenge: the transition times are not known exactly, and in some cases the path through the health states is not known.

In this manuscript we review existing approaches for modeling multi-state longitudinal data. …

Hierarchical Vector Auto-Regressive Models And Their Applications To Multi-Subject Effective Connectivity, Cristina Gorrostieta, Mark Fiecas, Hernando Ombao, Erin Burke, Steven Cramer

Mark Fiecas

Tools And Methods To Optimize The Analysis Of Telescopic Performance Metrics On Sofia, Steven R. Wilson, Holger Jakob, Stefan Teufel, Zaheer Ali, Jeffrey Van Cleve, Brian Eney, Greg Perryman

STAR Program Research Presentations

SOFIA is an infrared observatory mounted on a modified 747 engineered to do infrared astronomy at 45000 feet. The telescope equipment contains a number of sensors and stabilizers that allow the telescope to capture images while mounted in a moving plane. We have developed methods to analyze the performance of the telescope assembly that will help improve the stabilization and image capturing performance of the observatory. Here we present reusable methods to analyze telescope performance data that will enable improvements in the quality of the scientific data that is produced by the SOFIA. This poster focuses on the multi-flight performance …

Errata And Comments For: Generalized Estimating Equations, 2nd Ed, Joseph M. Hilbe, James W. Hardin

Joseph M Hilbe

Errata and Comments for Hardin & Hilbe, Generalized Estimating Equations, 2nd ed (published 10 Dec, 2012)

Modelling Locally Changing Variance Structured Time Series Data By Using Breakpoints Bootstrap Filtering, Rajan Lamichhane

Mathematics & Statistics Theses & Dissertations

Stochastic processes have applications in many areas such as oceanography and engineering. Special classes of such processes deal with time series of sparse data. Studies in such cases focus in the analysis, construction and prediction in parametric models. Here, we assume several non-linear time series with additive noise components, and the model fitting is proposed in two stages. The first stage identifies the density using all the clusters information, without specifying any prior knowledge of the underlying distribution function of the time series. The effect of covariates is controlled by fitting the linear regression model with serially correlated errors. In …

The Distortional Effects Of Temporal Aggregation On Granger Causality, Gulasekaran Rajaguru, Tilak Abeysinghe

Gulasekaran Rajaguru

Economists often have to use temporally aggregated data in causality tests. A number of theoretical studies have pointed out that temporal aggregation has distorting effects on causal inference. This paper provides a quantitative assessment of the magnitude of the distortions created by temporal aggregation by plugging in theoretical cross covariances into the limiting values of least squares estimates. Some Monte Carlo results and an application are provided to assess the impact in small samples. It is observed that in general the most distorting causal inferences are likely at low levels of temporal aggregation. At high levels of aggregation, causal information …

Nba Salaries: Assessing True Player Value, Michael Ghirardo

Statistics

This paper analyzes and calculates an advanced NBA statistic that is becoming more and more widely used in the NBA. The Adjusted plus-minus (APM) statistic measures a player’s contribution, independent of all other players on the court. The most appealing aspect to the APM is that it only attempts to capture how a team’s scoring margin changes with a particular player on and off the court. Scoring margin in basketball effects winning percentage greatly, so it only makes sense that players with high APM’s will increase their team’s scoring margin and, therefore, help win games. The APM statistic is not …

Emirical Assessment Of The Future Performance Of The S&P 500 Losers, Nicholas Powers

Statistics

In the Wall Street Journal in early 2013, there was an article posted by Andrew Bary that explored a trend in the previous 3 years of the S&P 500. The article pointed out that the average returns for the top 10 percentage decliners for 2009, 2010, and 2011 outperformed the S&P 500 for the first two weeks of the next year. These top 10 percentage decliners or losers well enough to bet on. This study looks to see if there is statistical evidence that the losers outperformed the S&P 500.

Discovering Exoplanets Through Hidden Markov Model Analysis, Jon Drobny

Rose-Hulman Undergraduate Research Publications

The goal for the project is to develop a Hidden Markov Model for the detection and characterization of extrasolar planets through the analysis of light curves.

Targeted Maximum Likelihood Estimation For Dynamic And Static Longitudinal Marginal Structural Working Models, Maya L. Petersen, Joshua Schwab, Susan Gruber, Nello Blaser, Michael Schomaker, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because …

Geographic And Temporal Epidemiology Of Campylobacteriosis, Jennifer Weisent

Doctoral Dissertations

Campylobacteriosis is a leading cause of gastroenteritis in the United States. The focus of this research was to (i) analyze and predict spatial and temporal patterns and associations for campylobacteriosis risk and (ii) compare the utility of advanced modeling methods. Laboratory-confirmed Campylobacter case data, obtained from the Foodborne Diseases Active Surveillance Network were used in all investigations.

We compared the accuracy of forecasting techniques for campylobacteriosis risk in Minnesota, Oregon and Georgia and found that time series regression, decomposition, and Box-Jenkins Autoregressive Integrated Moving Averages reliably predict monthly risk of infection for campylobacteriosis. Decomposition provided the fastest, most accurate, user-friendly …

Seasonal Decomposition For Geographical Time Series Using Nonparametric Regression, Hyukjun Gweon

Electronic Thesis and Dissertation Repository

A time series often contains various systematic effects such as trends and seasonality. These different components can be determined and separated by decomposition methods. In this thesis, we discuss time series decomposition process using nonparametric regression. A method based on both loess and harmonic regression is suggested and an optimal model selection method is discussed. We then compare the process with seasonal-trend decomposition by loess STL (Cleveland, 1979). While STL works well when that proper parameters are used, the method we introduce is also competitive: it makes parameter choice more automatic and less complex. The decomposition process often requires that …

Joint Outcome Modeling Using Shared Frailties With Application To Temporal Streamflow Data, Lihua Li

Electronic Thesis and Dissertation Repository

Recently there has been tremendous interest in the development of tools for joint analysis of longitudinal data and time-to-event data. This has gained emphasis particularly in clinical studies, where longitudinal measurements on a response may be recorded along with a time-to-event outcome. Joint analysis of multiple outcomes beyond longitudinal and survival have also been considered, for example, joint analysis of a variety of generalized linear models including continuous and count data, or continuous and binomial data. With joint analysis of multiple outcomes, the interest may be analysis of one outcome conditional on the others, or, more typically, analysis of all …

Analysis Of Continuous Longitudinal Data With Arma(1, 1) And Antedependence Correlation Structures, Sirisha Mushti

Mathematics & Statistics Theses & Dissertations

Longitudinal or repeated measure data are common in biomedical and clinical trials. These data are often collected on individuals at scheduled times resulting in dependent responses. Inference methods for studying the behavior of responses over time as well as methods to study the association with certain risk factors or covariates taking into account the dependencies are of great importance. In this research we focus our study on the analysis of continuous longitudinal data. To model the dependencies of the responses over time, we consider appropriate correlation structures generated by the stationary and non-stationary time-series models. We develop new estimation procedures …

Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 3: Attitudinal Analysis - Artist Housing And Space Survey, Mark Salling, Gregory Soltis, Charles Post, Sharon Bliss, Ellen Cyran

Ellen Cyran

A series of reports detailing the residential and work space location preferences of Cuyahoga county's artists.

Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 2: Profiles Of Artist Neighborhoods, Mark Salling, Gregory Soltis, Charles Post, Sharon Bliss, Ellen Cyran

Ellen Cyran

A series of reports detailing the residential and work space location preferences of Cuyahoga county's artists.

Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 1: Summary Report, Mark Salling, Gregory Soltis, Charles Post, Sharon Bliss, Ellen Cyran

Ellen Cyran

A series of reports detailing the residential and work space location preferences of Cuyahoga county's artists.