Statistical Models Commons^™

On Cluster Robust Models, José Bayoán Santiago Calderón

CGU Theses & Dissertations

Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches …

Global Warming Statistical Analysis, Jared Skinner

Williams Honors College, Honors Research Projects

This paper will investigate global warming and its effects on natural disasters. I will review the historic movements of climate change and activism, as well as the current discussions surrounding global warming. Secondly, I will examine various datasets, paying attention to the severity and frequency of specific natural disasters. I will then touch briefly on the topic of catastrophe modeling as it relates to the increased risk and losses associated with the discussed natural disasters and how those put the problem of global warming in a framework which financial and government institutions can grasp. I will also be analyzing economic …

Bayesian Analysis For The Intraclass Model And For The Quantile Semiparametric Mixed-Effects Double Regression Models, Duo Zhang

Dissertations, Master's Theses and Master's Reports

This dissertation consists of three distinct but related research projects. The first two projects focus on objective Bayesian hypothesis testing and estimation for the intraclass correlation coefficient in linear models. The third project deals with Bayesian quantile inference for the semiparametric mixed-effects double regression models. In the first project, we derive the Bayes factors based on the divergence-based priors for testing the intraclass correlation coefficient (ICC). The hypothesis testing of the ICC is used to test the uncorrelatedness in multilevel modeling, and it has not well been studied from an objective Bayesian perspective. Simulation results show that the two sorts …

Snakebite Dynamics Of Colombia: Effects Of Precipitation Seasonality Of Incidence, Carlos Cruz

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard

Electronic Theses and Dissertations

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A …

Building A Better Risk Prevention Model, Steven Hornyak

National Youth Advocacy & Resilience Conference

This presentation chronicles the work of Houston County Schools in developing a risk prevention model built on more than ten years of longitudinal student data. In its second year of implementation, Houston At-Risk Profiles (HARP), has proven effective in identifying those students most in need of support and linking them to interventions and supports that lead to improved outcomes and significantly reduces the risk of failure.

New Approaches To Model Simulated Spatio-Temporal Moran's Index, Nhan Bu, Jennifer Lorio, Norou Diawara, Kumar Das, Lance Waller

Mathematics & Statistics Faculty Publications

The Moran's index is a statistic that measures spatial autocorrelation; it quantifies the degree of dispersion (or clustering) of objects in space. However, when investigating data over a general area, a single global Moran statistic may not give a sufficient summary of the spread, behavior, features or latent surfaces shared by neighboring areas; rather, by partitioning the area and taking the Moran statistic of each divided subareas, we can discover patterns of the local neighbors not otherwise apparent. In this paper, we present a simulation experiment where the local Moran values are computed and a time variable is added to …

Improved Methods And Selecting Classification Types For Time-Dependent Covariates In The Marginal Analysis Of Longitudinal Data, I-Chen Chen

Theses and Dissertations--Epidemiology and Biostatistics

Generalized estimating equations (GEE) are popularly utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when certain types of time-dependent covariates are presented, these equations can be biased unless an independence working correlation structure is employed. Moreover, in this case regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches using the generalized method of moments or quadratic inference functions have been proposed for utilizing all valid moment conditions. However, we have found that …

Penalized Mixed-Effects Ordinal Response Models For High-Dimensional Genomic Data In Twins And Families, Amanda E. Gentry

Theses and Dissertations

The Brisbane Longitudinal Twin Study (BLTS) was being conducted in Australia and was funded by the US National Institute on Drug Abuse (NIDA). Adolescent twins were sampled as a part of this study and surveyed about their substance use as part of the Pathways to Cannabis Use, Abuse and Dependence project. The methods developed in this dissertation were designed for the purpose of analyzing a subset of the Pathways data that includes demographics, cannabis use metrics, personality measures, and imputed genotypes (SNPs) for 493 complete twin pairs (986 subjects.) The primary goal was to determine what combination of SNPs and …

Burden Of Atopic Dermatitis In The United States: Analysis Of Healthcare Claims Data In The Commercial, Medicare, And Medi-Cal Databases, Sulena Shrestha, Raymond Miao, Li Wang, Jingdong Chao, Huseyin Yuce, Wenhui Wei

Publications and Research

Comparative data on the burden of atopic dermatitis (AD) in adults relative to the general population are limited. We performed a large-scale evaluation of the burden of disease among US adults with AD relative to matched non-AD controls, encompassing comorbidities, healthcare resource utilization (HCRU), and costs, using healthcare claims data. The impact of AD disease severity on these outcomes was also evaluated.

On The Three Dimensional Interaction Between Flexible Fibers And Fluid Flow, Bogdan Nita, Ryan Allaire

Department of Mathematics Faculty Scholarship and Creative Works

In this paper we discuss the deformation of a flexible fiber clamped to a spherical body and immersed in a flow of fluid moving with a speed ranging between 0 and 50 cm/s by means of three dimensional numerical simulation developed in COMSOL . The effects of flow speed and initial configuration angle of the fiber relative to the flow are analyzed. A rigorous analysis of the numerical procedure is performed and our code is benchmarked against well established cases. The flow velocity and pressure are used to compute drag forces upon the fiber. Of particular interest is the behavior …

Modeling Volatility Of Financial Time Series Using Arc Length, Benjamin H. Hoerlein

College of Graduate Studies: Theses & Dissertations

This thesis explores how arc length can be modeled and used to measure the risk involved with a financial time series. Having arc length as a measure of volatility can help an investor in sorting which stocks are safer/riskier to invest in. A Gamma autoregressive model of order one(GAR(1)) is proposed to model arc length series. Kernel regression based bias correction is studied when model parameters are estimated using method of moment procedure. As an application, a model-based clustering involving thirty different stocks is presented using k-means++ and hierarchical clustering techniques.

A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz

Doctor of Business Administration Dissertations

At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with …

Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae

The Summer Undergraduate Research Fellowship (SURF) Symposium

Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, the S.M.A.R.T system provides the analyst with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning …

Multilevel Models For Longitudinal Data, Aastha Khatiwada

Electronic Theses and Dissertations

Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each …

Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku

Electronic Theses and Dissertations

The evolution of big data has led to financial time series becoming increasingly complex, noisy, non-stationary and nonlinear. Takens theorem can be used to analyze and forecast nonlinear time series, but even small amounts of noise can hopelessly corrupt a Takens approach. In contrast, Singular Spectrum Analysis is an excellent tool for both forecasting and noise reduction. Fortunately, it is possible to combine the Takens approach with Singular Spectrum analysis (SSA), and in fact, estimation of key parameters in Takens theorem is performed with Singular Spectrum Analysis. In this thesis, we combine the denoising abilities of SSA with the Takens …

Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret

UW Biostatistics Working Paper Series

We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the …

Multi-State Models With Missing Covariates, Wenjie Lou

Theses and Dissertations--Statistics

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models.

Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the …

Space-Time Modelling Of Emerging Infectious Diseases: Assessing Leptospirosis Risk In Sri Lanka, Cameron C F Plouffe

Theses and Dissertations (Comprehensive)

In this research, models were developed to analyze leptospirosis incidence in Sri Lanka and its relation to rainfall. Before any leptospirosis risk models were developed, rainfall data were evaluated from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. Four spatial interpolation techniques were compared: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from …

Preparedness Of Hospitals In The Republic Of Ireland For An Influenza Pandemic, An Infection Control Perspective, Mary Reidy, Fiona Ryan, Dervla Hogan, Seán Lacey, Claire Buckley

Department of Mathematics Publications

When an influenza pandemic occurs most of the population is susceptible and attack rates can range as high as 40–50 %. The most important failure in pandemic planning is the lack of standards or guidelines regarding what it means to be ‘prepared’. The aim of this study was to assess the preparedness of acute hospitals in the Republic of Ireland for an influenza pandemic from an infection control perspective.

Time Series Analysis For Psychological Research: Examining And Forecasting Change, Andrew T. Jebb, Louis Tay, Wei Wang, Qiming Huang

Publications and Research

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that …

Using Spatiotemporal Methods To Fill Gaps In Energy Usage Interval Data, Kristin K. Graves

Theses and Dissertations

Researchers analyzing spatiotemporal or panel data, which varies both in location and over time, often find that their data has holes or gaps. This thesis explores alternative methods for filling those gaps and also suggests a set of techniques for evaluating those gap-filling methods to determine which works best.

The Effects Of Quantitative Easing In The United States: Implications For Future Central Bank Policy Makers, Matthew Q. Rubino

Senior Honors Projects, 2010-2019

The purpose of this thesis is to examine the effects of the Federal Reserve’s recent bond buying programs, specifically Quantitative Easing 1, Quantitative Easing 2, Operation Twist (or the Fed’s Maturity Extension Program), and Quantitative Easing 3. In this study, I provide a picture of the economic landscape leading up to the deployment of the programs, an overview of quantitative easing including each program’s respective objectives, and how and why the Fed decided to implement the programs. Using empirical analysis, I measure each program’s effectiveness by applying four models including a yield curve model, an inflation model, a money supply …

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 …

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 …

Short-Term Inflation Forecasting Models For Nigeria, Sani I. Doguwa, Sarah O. Alade

CBN Journal of Applied Statistics (JAS)

Short-term inflation forecasting is an essential component of the monetary policy projections at the Central Bank of Nigeria. This paper proposes four short-term headline inflation forecasting models using the SARIMA and SARIMAX processes and compares their performance using the pseudo-out-of-sample forecasting procedure over July 2011 to September 2013. According to the results the best forecasting performance is demonstrated by the model based on the all items CPI estimated using the SARIMAX model. This model is, therefore, recommended for use in short-term forecasting of headline inflation in Nigeria. The forecasting performance up to eight months ahead, of the models based on …

An Efficient Two Sample Capture-Recapture Model With High Recaptures, Danjuma Jibasen, Yusuf J. Adams

CBN Journal of Applied Statistics (JAS)

This paper proposed an efficient two sample capture-recapture model (Ma) with high recaptures and compared it with the existing models like the model of no factor effect (Mo), behavioral response model (Mb) and the Petersen model (Ms), using simulated data. We found that the Petersen model provides a better estimate of the population size when the observations follow a hypergeometric distribution and the population is overestimated when recapture is high. It was also found that the proposed model provides a better estimator of the population size than the existing ones when the recapture is high. This model is particularly useful …

Causal Relationship Between Stock Market Index And Exchange Rate: Evidence From Nigeria, Abdulrasheed Zubair

CBN Journal of Applied Statistics (JAS)

This paper uses Johansen’s cointegration to test for the possibility of cointegration and Granger-causality to estimate the causal relationship between stock market index and monetary indicators (exchange rate and M2) before and during the global financial crisis for Nigeria, using monthly data for the period 2001–2011. Results suggest absence of long-run relationship before and during the crisis. The Granger-causality tests show a uni-directional causality running from M2 to ASI before the crisis while during the period of the crisis there is absence of causality between the variables. This suggests that ASI show responsiveness to M2. Thus, absence of the direct …

Investigating Chaos In The Nigerian Asset And Resource Management (Arm) Discovery Fund, Ibiyinka A. Fuwape, Samuel T. Ogunjo

CBN Journal of Applied Statistics (JAS)

This paper investigates chaos in a Nigerian mutual fund, Asset and Resource Management Company Limited (ARM) for a period of eleven years. The existence of chaotic signals in the data was identified by the reconstruction of the phase space of the daily closing price of the fund and the delay time was quantified using mutual information function and the embedding dimension by the false nearest neighbours, where the values were identified to be 15 and 20 respectively. The presence of chaotic signals in the ARM data was further confirmed by the correlation dimension method which yielded a dimension of 2.2 …

Modeling The Nigerian Inflation Rates Using Periodogram And Fourier Series Analysis, Chukwuemeka O. Omekara,, Emmanuel J. Ekpenyong, Micheal P. Ekerete

CBN Journal of Applied Statistics (JAS)

This work considers the application of Periodogram and Fourier Series Analysis to model all-items monthly inflation rates in Nigeria from 2003 to 2011. The main objectives are to identify inflation cycles, fit a suitable model to the data and make forecasts of future values. To achieve these objectives, monthly all-items inflation rates for the period were obtained from the Central Bank of Nigeria (CBN) website. Periodogram and Fourier series methods of analysis are used to analyze the data. Based on the analysis, it was found that inflation cycle within the period was fifty one (51) months, which coincides with the …