Applied Statistics Commons^™

Exonest: Bayesian Model Selection Applied To The Detection And Characterization Of Exoplanets Via Photometric Variations, Ben Placek, Kevin H. Knuth, Daniel Angerhausen

Physics Faculty Scholarship

EXONEST is an algorithm dedicated to detecting and characterizing the photometric signatures of exoplanets, which include reflection and thermal emission, Doppler boosting, and ellipsoidal variations. Using Bayesian inference, we can test between competing models that describe the data as well as estimate model parameters. We demonstrate this approach by testing circular versus eccentric planetary orbital models, as well as testing for the presence or absence of four photometric effects. In addition to using Bayesian model selection, a unique aspect of EXONEST is the potential capability to distinguish between reflective and thermal contributions to the light curve. A case study is …

Stratified Meta-Analysis To Examine Data Biases In Lung Cancer Studies Of Refinery Workers, Sherman Selix

Yale Day of Data

Petroleum refineries employ a variety of workers who historically experienced different potentials for asbestos exposure depending on job tasks. Associations between petroleum refinery work and lung cancer related to occupational asbestos exposure have been quantified among various locations, corporations, and time periods. To combine the data from several individual refinery studies and examine an overall effect, a systematic review and stratified meta-analysis was employed. Using set search terms among four databases, 112 potential publications were identified, of which 29 qualified for meta-analysis. Risk estimates and confidence intervals were extracted from these publications to construct four separate datasets. Inverse variance weighting …

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 …

Using Remote Sensing Data To Predict The Spread Of Mosquito Borne Disease, Mary Ellen O'Donnell, Erika Podest

STAR Program Research Presentations

There is interest in how environmental variables derived from satellite data such as temperature, vegetation cover, and precipitation correlate to vector borne disease occurrence such as malaria and dengue fever. This study will be carried out using a decision tree based open source software called Random Forests to find correlations between the remote sensing variables and mosquito abundance. Software will be written in C# to take large amounts of data from the NASA satellite database and automatically format it for the Random Forest Software input. Correlations found, using Random Forests, between disease incidence and the variables can be used as …

The Path To The Sea: Leatherback Hatchling Orientation At Sandy Point National Wildlife Refuge, Christina Macmillan, Kelly Stewart

STAR Program Research Presentations

Once sea turtle hatchlings emerge from their nest, they must find their way to the ocean by using cues such as a bright horizon and the slope of the beach. While moving toward the water, hatchlings often must navigate past predators and through vegetation, sticks, footprints in the sand, and other dangers such as ghost crab holes. Sometimes hatchlings become confused (or disoriented) and turn in circles to find the right route to the water. Sea turtle hatchlings also may become disoriented as a result of human impacts such as town lights or trash. The purpose of our experiment was …

Light Pollution Research Through Citizen Science, John Kanemoto

STAR Program Research Presentations

Light pollution (LP) can disrupt and/or degrade the health of all living things, as well as, their environments. The goal of my research at the NOAO was to check the accuracy of the citizen science LP reporting systems entitled: Globe at Night (GaN), Dark Sky Meter (DSM), and Loss of the Night (LoN). On the GaN webpage, the darkness of the night sky (DotNS) is reported by selecting a magnitude chart. Each magnitude chart has a different density/number of stars around a specific constellation. The greater number of stars implies a darker night sky. Within the DSM iPhone application, a …

Analyses Of 2002-2013 China’S Stock Market Using The Shared Frailty Model, Chao Tang

Electronic Theses and Dissertations

This thesis adopts a survival model to analyze China’s stock market. The data used are the capitalization-weighted stock market index (CSI 300) and the 300 stocks for creating the index. We define the recurrent events using the daily return of the selected stocks and the index. A shared frailty model which incorporates the random effects is then used for analyses since the survival times of individual stocks are correlated. Maximization of penalized likelihood is presented to estimate the parameters in the model. The covariates are selected using the Akaike information criterion (AIC) and the variance inflation factor (VIF) to avoid …

Improvements On Segment Based Contours Method For Dna Microarray Image Segmentation, Yang Li

Doctoral Dissertations

DNA microarray is an efficient biotechnology tool for scientists to measure the expression levels of large numbers of genes, simultaneously. To obtain the gene expression, microarray image analysis needs to be conducted. Microarray image segmentation is a fundamental step in the microarray analysis process. Segmentation gives the intensities of each probe spot in the array image, and those intensities are used to calculate the gene expression in subsequent analysis procedures. Therefore, more accurate and efficient microarray image segmentation methods are being pursued all the time.

In this dissertation, we are making efforts to obtain more accurate image segmentation results. We …

Common Method Variance: An Experimental Manipulation, Alison Wall

Doctoral Dissertations

Although common method variance has been a subject of research concern for over fifty years, its influence on study results is still not well understood. Common method variance concerns are frequently cited as an issue in the publication of self-report data; yet, there is no consensus as to when, or if, common method variance creates bias. This dissertation examines common method variance by approaching it from an experimental standpoint. If groups of respondents can be influenced to vary their answers to survey items based upon the presence or absence of procedural remedies, a better understanding of common method variance can …

Modeling Spatial Covariance Functions, Inkyung Choi

Open Access Dissertations

Covariance modeling plays a key role in the spatial data analysis as it provides important information about the dependence structure of underlying processes and determines performance of spatial prediction. Various parametric models have been developed to accommodate the idiosyncratic features of a given dataset. However, the parametric models may impose unjustified restrictions to the covariance structure and the procedure of choosing a specific model is often ad-hoc. In the first part of the dissertation, a new nonparametric covariance model that can avoid the choice of parametric forms is proposed. The estimator is obtained via a nonparametric approximation of completely monotone …

The Impact Of Student Performance On Large-Scale Assessments: A View Of Long-Term Health, Career, And Societal Outcomes, Roman Usatin

Seton Hall University Dissertations and Theses (ETDs)

This study examined the predictive power of student growth for large-scale assessments on meaningful life outcomes, focusing on the three categories of health, career, and societal involvement. Analysis was conducted using the NELS:88/00 dataset–a longitudinal study that followed a nationally-representative sample of over 12,000 eighth grade students from 1988 to 2000, until the students were 26 years old and entered into the work force. The large-scale assessment variables included math and reading performance in the 1988 cognitive batteries administered by NELS. To gauge growth levels, I generated Student Growth Percentiles (SGP) from tests administered by NELS from 1988 to 1992. …

Simulating Influenza Transmission With Network Data, Henry V. Bongiovi

Statistics

Simulating Influenza Transmission with Real Network Data

Henry Bongiovi BS Statistics, California Polytechnic State University, San Luis Obispo

[email protected]

Keywords: Network Data, Simulation, Education, Influenza, Epidemic

Disease has been humanities arch rival since the dawn of our existence. As such, we have been trying our best to understand its spread and proliferation. One of the most common diseases, Influenza, is also one of the most complex. To understand the complexities of its spread would greatly improve our ability to combat it and other diseases like it. Using R in conjunction with the package statnet, I have created a simulation of …

A Proof Of Concept For Crowdsourcing Color Perception Experiments, Ryan Nathaniel Mcleod

Master's Theses

Accurately quantifying the human perception of color is an unsolved prob- lem. There are dozens of numerical systems for quantifying colors and how we as humans perceive them, but as a whole, they are far from perfect. The ability to accurately measure color for reproduction and verification is critical to indus- tries that work with textiles, paints, food and beverages, displays, and media compression algorithms. Because the science of color deals with the body, mind, and the subjective study of perception, building models of color requires largely empirical data over pure analytical science. Much of this data is extremely dated, …

Musical Missteps: The Severity Of The Sophomore Slump In The Music Industry, Shane M. Zackery

Scripps Senior Theses

This study looks at alternative models of follow-up album success in order to determine if there is a relationship between the decrease in Metascore ratings (assigned by Metacritic.com) between the first and second album for a musician or band and the 1) music genre or 2) the number of years between the first and second album release. The results support the dominant thought, which suggests that neither belonging to a certain genre of music nor waiting more or less time to drop the second album makes an artist more susceptible to the Sophomore Slump. This finding is important because it …

Bias And Precision Of The Squared Canonical Correlation Coefficient Under Nonnormal Data Condition, Lesley F. Leach, Robin K. Henson

Journal of Modern Applied Statistical Methods

Monte Carlo methods were employed to investigate the effect of nonnormality on the bias associated with the squared canonical correlation coefficient (R_c²). The majority of R_c² estimates were found to be extremely biased, but the magnitude of bias was impacted little by the degree of nonnormality.

Stochastic Randomized Response Model For A Quantitative Sensitive Random Variable, Sarjinder Singh, Stephen A. Sedory

Journal of Modern Applied Statistical Methods

A new stochastic randomized response model is introduced that is useful for estimating the population mean of a sensitive quantitative variable. The proposed stochastic randomized response model is an extension of the stochastic randomized response model from a qualitative sensitive variable to a quantitative variable found in Singh (2002). The stochastic nature of a randomized response device helps increase a respondent’s cooperation while collecting information on sensitive variables in a society. The Bar-Lev, Bobovitch, and Boukai (2004) model is shown to be a special case of the proposed model.

Ridge Regression In Calibration Models With Symmetric Padding Extension-Daubechies Wavelet Transform Preprocessing, Nurwiani, S Sunaryo, Setiawan, B W. Otok

Journal of Modern Applied Statistical Methods

Wavelet transformation is commonly used in calibration models as a preprocessing step. This preprocessing does not involve all results of a spectrum discretization; consequently, a lot of information can be missing. To avoid missing information, a symmetric padding extension (SPE) can be used to place all data points into dyadic scales, however, high dimensional discretization points need to be reduced. Dimension reduction can be performed with Daubechies wavelet transformation (DWT). Scale function and Daubechies wavelet are continuous functions, thus they perform a faster approximation. SPE-DWT preprocessing combines SPE and DWT. Multicollinearity often occurs in calibration models; the ridge regression (RR) …

Statistical Power Of Alternative Structural Models For Comparative Effectiveness Research: Advantages Of Modeling Unreliability, Emil N. Coman, Eugen Iordache, Lisa Dierker, Judith Fifield, Jean J. Schensul, Suzanne Suggs, Russell Barbour

Journal of Modern Applied Statistical Methods

The advantages of modeling the unreliability of outcomes when evaluating the comparative effectiveness of health interventions is illustrated. Adding an action-research intervention component to a regular summer job program for youth was expected to help in preventing risk behaviors. A series of simple two-group alternative structural equation models are compared to test the effect of the intervention on one key attitudinal outcome in terms of model fit and statistical power with Monte Carlo simulations. Some models presuming parameters equal across the intervention and comparison groups were under- powered to detect the intervention effect, yet modeling the unreliability of the outcome …

A Flexible Method For Conducting Power Analysis For Two- And Three-Level Hierarchical Linear Models In R, Yi Pan, Matthew T. Mcbee

Journal of Modern Applied Statistical Methods

A general approach for conducting power analysis in two- and three-level hierarchical linear models (HLMs) is described. The method can be used to perform power analysis to detect fixed effects at any level of a HLM with dichotomous or continuous covariates. It can easily be extended to perform power analysis for functions of parameters. Important steps in the derivation of this approach are illustrated and numerical examples are provided. Sample code implementing this approach is provided using the free program R.

Estimation Of Reliability In Multicomponent Stress-Strength Based On Generalized Rayleigh Distribution, Gadde Srinivasa Rao

Journal of Modern Applied Statistical Methods

A multicomponent system of k components having strengths following k- independently and identically distributed random variables x₁, x₂, ..., x_k and each component experiencing a random stress Y is considered. The system is regarded as alive only if at least s out of k (s < k) strengths exceed the stress. The reliability of such a system is obtained when strength and stress variates are given by a generalized Rayleigh distribution with different shape parameters. Reliability is estimated using the maximum likelihood (ML) method of estimation in samples drawn from strength and stress …