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Articles 1 - 30 of 64
Full-Text Articles in Statistical Methodology
Application Of Bradford’S Law Of Scattering On Research Publication In Astronomy & Astrophysics Of India, Satish Kumar, Senthilkumar R.
Application Of Bradford’S Law Of Scattering On Research Publication In Astronomy & Astrophysics Of India, Satish Kumar, Senthilkumar R.
Library Philosophy and Practice (e-journal)
The present study is focused on examining the application of Bradford’s law of scattering on research articles published in the field of Astronomy & Astrophysics by Indian scientist during 1988-2017. The bibliographic data was retrieved from Web of Science (WoS) bibliographic data base for different period of time. Total 18,877 journal’s article have been published by Indian scientist in the field of Astronomy & Astrophysics during 1988-2017 which was further retrieved and analyzed separately for different blocks of 10 years as well as for 30 years consolidated too. The core journal of the field was identified. The Bradford law of …
Different Estimation Methods For The Basic Independent Component Analysis Model, Zhenyi An
Different Estimation Methods For The Basic Independent Component Analysis Model, Zhenyi An
Arts & Sciences Electronic Theses and Dissertations
Inspired by classic cocktail-party problem, the basic Independent Component Analysis (ICA) model is created. What differs Independent Component Analysis (ICA) from other kinds of analysis is the intrinsic non-Gaussian assumption of the data. Several approaches are proposed based on maximizing the non-Gaussianity of the data, which is measured by kurtosis, mutual information, and others. With each estimation, we need to optimize the functions of expectations of non-quadratic functions since it can help us to access the higher-order statistics of non-Gaussian part of the data. In this thesis, our goal is to review the one of the most efficient estimation methods, …
Anisotropic Kernel Smoothing For Change-Point Data With An Analysis Of Fire Spread Rate Variability, John Ronald James Thompson
Anisotropic Kernel Smoothing For Change-Point Data With An Analysis Of Fire Spread Rate Variability, John Ronald James Thompson
Electronic Thesis and Dissertation Repository
Wildland fires are natural disturbances that enable the renewal of forests. However, these fires also place public safety and property at risk. Understanding forest fire spread in any region of Canada is critical to promoting forest health, and protecting human life and infrastructure. In 2014, Ontario updated its Wildland Fire Management Strategy, moving away from ``zone-based" decision making to ``appropriate response" decision making. This new strategy calls for an assessment of the risks and benefits of every wildland fire reported in the province. My research places the emphasis on the knowledge and understanding of fire spread rates and their variabilities. …
Bias Assessment And Reduction In Kernel Smoothing, Wenkai Ma
Bias Assessment And Reduction In Kernel Smoothing, Wenkai Ma
Electronic Thesis and Dissertation Repository
When performing local polynomial regression (LPR) with kernel smoothing, the choice of the smoothing parameter, or bandwidth, is critical. The performance of the method is often evaluated using the Mean Square Error (MSE). Bias and variance are two components of MSE. Kernel methods are known to exhibit varying degrees of bias. Boundary effects and data sparsity issues are two potential problems to watch for. There is a need for a tool to visually assess the potential bias when applying kernel smooths to a given scatterplot of data. In this dissertation, we propose pointwise confidence intervals for bias and demonstrate a …
Variational Approximations For Density Deconvolution, Yue Chang
Variational Approximations For Density Deconvolution, Yue Chang
Doctoral Dissertations
This thesis considers the problem of density estimation when the variables of interest are subject to measurement error. The measurement error is assumed to be additive and homoscedastic. We specify the density of interest by a Dirichlet Process Mixture Model and establish variational approximation approaches to the density deconvolution problem. Gaussian and Laplacian error distributions are considered, which are representatives of supersmooth and ordinary smooth distributions, respectively. We develop two variational approximation algorithms for Gaussian error deconvolution and one variational approximation algorithm for Laplacian error deconvolution. Their performances are compared to deconvoluting kernels and Monte Carlo Markov Chain method by …
Resource Assessment Report Temperate Demersal Elasmobranch Resource Of Western Australia, Matias Braccini, Nick Blay, S. A. Hesp, Brett Molony
Resource Assessment Report Temperate Demersal Elasmobranch Resource Of Western Australia, Matias Braccini, Nick Blay, S. A. Hesp, Brett Molony
Fisheries research reports
This document provides a cumulative description and assessment of the TDER and all of the fishing activities (i.e. fisheries / fishing sectors) affecting this resource in WA. Future Resource Assessment Reports will assess the Statewide Sharks and Rays Resource. The report is focused on the temperate indicator species (whiskery, gummy, dusky and sandbar sharks) used to assess the suites of demersal sharks and rays that comprise this resource. These species are primarily captured by demersal gillnets used in the TDGDLF that operate in the West Coast and South Coast Bioregions. For the North Coast bioregion, no commercial fishing for sharks …
Real-Time Dengue Forecasting In Thailand: A Comparison Of Penalized Regression Approaches Using Internet Search Data, Caroline Kusiak
Real-Time Dengue Forecasting In Thailand: A Comparison Of Penalized Regression Approaches Using Internet Search Data, Caroline Kusiak
Masters Theses
Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions in settings with severely …
Analysis Of Covariance (Ancova) In Randomized Trials: More Precision, Less Conditional Bias, And Valid Confidence Intervals, Without Model Assumptions, Bingkai Wang, Elizabeth Ogburn, Michael Rosenblum
Analysis Of Covariance (Ancova) In Randomized Trials: More Precision, Less Conditional Bias, And Valid Confidence Intervals, Without Model Assumptions, Bingkai Wang, Elizabeth Ogburn, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
Covariate adjustment" in the randomized trial context refers to an estimator of the average treatment effect that adjusts for chance imbalances between study arms in baseline variables (called “covariates"). The baseline variables could include, e.g., age, sex, disease severity, and biomarkers. According to two surveys of clinical trial reports, there is confusion about the statistical properties of covariate adjustment. We focus on the ANCOVA estimator, which involves fitting a linear model for the outcome given the treatment arm and baseline variables, and trials with equal probability of assignment to treatment and control. We prove the following new (to the best …
Estimation In High-Dimensional Factor Models With Structural Instabilities, Wen Gao
Estimation In High-Dimensional Factor Models With Structural Instabilities, Wen Gao
Major Papers
In this major paper, we use high-dimensional models to analyze macroeconomic data which is in influenced by the break point. In particular, we consider to detect the break point and study the changes of the number of factors and the factor loadings with the structural instability.
Concretely, we propose two factor models which explain the processes of pre- and post- break periods. Then, we consider the break point as known or unknown. In both situations, we derive the shrinkage estimators by minimizing the penalized least square function and calculate the estimators of the numbers of pre- and post- break factors …
Identifying Key Factors Associated With High Risk Asthma Patients To Reduce The Cost Of Health Resources Utilization, Amani Ahmad
Identifying Key Factors Associated With High Risk Asthma Patients To Reduce The Cost Of Health Resources Utilization, Amani Ahmad
LSU Master's Theses
Asthma is associated with frequent use of primary health services and places a burden on the United States economy. Identifying key factors associated with increased cost of asthma is an essential step to improve practices of asthma management.
The aim of this study was to identify factors associated with over utilization of primary health services and increased cost via claims data and to explore the effectiveness of case management program in reducing overall asthma related cost.
Claims data analysis for Medicaid insured asthma patients in Louisiana was conducted. Asthma patients were identified using their ICD-9 and ICD-10 codes, forward variable …
Season-Ahead Forecasting Of Water Storage And Irrigation Requirements – An Application To The Southwest Monsoon In India, Arun Ravindranath, Naresh Devineni, Upmanu Lall, Paulina Concha Larrauri
Season-Ahead Forecasting Of Water Storage And Irrigation Requirements – An Application To The Southwest Monsoon In India, Arun Ravindranath, Naresh Devineni, Upmanu Lall, Paulina Concha Larrauri
Publications and Research
Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years …
Demonstrating The Efficacy Of The Health Sciences And Technology Academy: Using Archival Standardized Test Scores To Analyze An Ost College-Preparatory Program For Underserved Youth, Feon Smith, Sherron Mckendall, Ann Chester, Bethany Hornbeck, Alan Mckendall
Demonstrating The Efficacy Of The Health Sciences And Technology Academy: Using Archival Standardized Test Scores To Analyze An Ost College-Preparatory Program For Underserved Youth, Feon Smith, Sherron Mckendall, Ann Chester, Bethany Hornbeck, Alan Mckendall
Faculty & Staff Scholarship
To combat educational and health disparities, out-of-school-time (OST) STEM enrichment programs provide services to underserved youth to encourage them to pursue college and health careers. This article describes a study conducted to determine if the Health Sciences and Technology Academy (HSTA) program participants who receive year-round educational interventions to prepare them for STEM and health sciences majors performed better on the West Virginia Educational Standards Test (WESTEST2) than non-participants. This study provides descriptive and inferential statistics, specifically one-way ANOVAs with one-to-one matching based on grade level, gender, race, and GPA at the end of the 8th grade year for 336 …
Minimizing The Perceived Financial Burden Due To Cancer, Hassan Azhar, Zoheb Allam, Gino Varghese, Daniel W. Engels, Sajiny John
Minimizing The Perceived Financial Burden Due To Cancer, Hassan Azhar, Zoheb Allam, Gino Varghese, Daniel W. Engels, Sajiny John
SMU Data Science Review
In this paper, we present a regression model that predicts perceived financial burden that a cancer patient experiences in the treatment and management of the disease. Cancer patients do not fully understand the burden associated with the cost of cancer, and their lack of understanding can increase the difficulties associated with living with the disease, in particular coping with the cost. The relationship between demographic characteristics and financial burden were examined in order to better understand the characteristics of a cancer patient and their burden, while all subsets regression was used to determine the best predictors of financial burden. Age, …
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
SMU Data Science Review
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …
Robust Inference For The Stepped Wedge Design, James P. Hughes, Patrick J. Heagerty, Fan Xia, Yuqi Ren
Robust Inference For The Stepped Wedge Design, James P. Hughes, Patrick J. Heagerty, Fan Xia, Yuqi Ren
UW Biostatistics Working Paper Series
Based on a permutation argument, we derive a closed form expression for an estimate of the treatment effect, along with its standard error, in a stepped wedge design. We show that these estimates are robust to misspecification of both the mean and covariance structure of the underlying data-generating mechanism, thereby providing a robust approach to inference for the treatment effect in stepped wedge designs. We use simulations to evaluate the type I error and power of the proposed estimate and to compare the performance of the proposed estimate to the optimal estimate when the correct model specification is known. The …
Generalizing Multistage Partition Procedures For Two-Parameter Exponential Populations, Rui Wang
Generalizing Multistage Partition Procedures For Two-Parameter Exponential Populations, Rui Wang
University of New Orleans Theses and Dissertations
ANOVA analysis is a classic tool for multiple comparisons and has been widely used in numerous disciplines due to its simplicity and convenience. The ANOVA procedure is designed to test if a number of different populations are all different. This is followed by usual multiple comparison tests to rank the populations. However, the probability of selecting the best population via ANOVA procedure does not guarantee the probability to be larger than some desired prespecified level. This lack of desirability of the ANOVA procedure was overcome by researchers in early 1950's by designing experiments with the goal of selecting the best …
A Comparison Of R, Sas, And Python Implementations Of Random Forests, Breckell Soifua
A Comparison Of R, Sas, And Python Implementations Of Random Forests, Breckell Soifua
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
The Random Forest method is a useful machine learning tool developed by Leo Breiman. There are many existing implementations across different programming languages; the most popular of which exist in R, SAS, and Python. In this paper, we conduct a comprehensive comparison of these implementations with regards to the accuracy, variable importance measurements, and timing. This comparison was done on a variety of real and simulated data with different classification difficulty levels, number of predictors, and sample sizes. The comparison shows unexpectedly different results between the three implementations.
Generalized Spatiotemporal Modeling And Causal Inference For Assessing Treatment Effects For Multiple Groups For Ordinal Outcome., Soutik Ghosal
Generalized Spatiotemporal Modeling And Causal Inference For Assessing Treatment Effects For Multiple Groups For Ordinal Outcome., Soutik Ghosal
Electronic Theses and Dissertations
This dissertation consists of three projects and can be categorized in two broad research areas: generalized spatiotemporal modeling and causal inference based on observational data. In the first project, I introduce a Bayesian hierarchical mixed effect hurdle model with a nested random effect structure to model the count for primary care providers and understand their spatial and temporal variation. This study further enables us to identify the health professional shortage areas and the possible impacting factors. In the second project, I have unified popular parametric and nonparametric propensity score-based methods to assess the treatment effect of multiple groups for ordinal …
Bayesian Analytical Approaches For Metabolomics : A Novel Method For Molecular Structure-Informed Metabolite Interaction Modeling, A Novel Diagnostic Model For Differentiating Myocardial Infarction Type, And Approaches For Compound Identification Given Mass Spectrometry Data., Patrick J. Trainor
Electronic Theses and Dissertations
Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, …
Predictions Generated From A Simulation Engine For Gene Expression Micro-Arrays For Use In Research Laboratories, Gopinath R. Mavankal, John Blevins, Dominique Edwards, Monnie Mcgee, Andrew Hardin
Predictions Generated From A Simulation Engine For Gene Expression Micro-Arrays For Use In Research Laboratories, Gopinath R. Mavankal, John Blevins, Dominique Edwards, Monnie Mcgee, Andrew Hardin
SMU Data Science Review
In this paper we introduce the technical components, the biology and data science involved in the use of microarray technology in biological and clinical research. We discuss how laborious experimental protocols involved in obtaining this data used in laboratories could benefit from using simulations of the data. We discuss the approach used in the simulation engine from [7]. We use this simulation engine to generate a prediction tool in Power BI, a Microsoft, business intelligence tool for analytics and data visualization [22]. This tool could be used in any laboratory using micro-arrays to improve experimental design by comparing how predicted …
Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis
Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis
SMU Data Science Review
A quantitative analysis will be performed on experiments utilizing three different tools used for Data Science. The analysis will include replication of analysis along with comparisons of code length, output, and results. Qualitative data will supplement the quantitative findings. The conclusion will provide data support guidance on the correct tool to use for common situations in the field of Data Science.
Hierarchical Bayesian Data Fusion Using Autoencoders, Yevgeniy Vladimirovich Reznichenko
Hierarchical Bayesian Data Fusion Using Autoencoders, Yevgeniy Vladimirovich Reznichenko
Master's Theses (2009 -)
In this thesis, a novel method for tracker fusion is proposed and evaluated for vision-based tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, semi supervised learning approaches are used to partition data and to train a deep neural network that is capable of capturing normal visual tracking operation and is able to detect anomalous data. We compare various methods by examining their respective receiver operating conditions (ROC) curves, which represent the trade off between specificity and sensitivity for various detection threshold levels. Next, we incorporate the trained neural networks into an existing data …
Australian Herring And West Australian Salmon Scientific Workshop Report, October 2017, Department Of Primary Industries And Regional Development, Western Australia
Australian Herring And West Australian Salmon Scientific Workshop Report, October 2017, Department Of Primary Industries And Regional Development, Western Australia
Fisheries research reports
No abstract provided.
Improving Shewhart Control Chart Performance In The Presence Of Measurement Error Using Multiple Measurements And Two-Stage Sampling, Kenneth W. Linna
Improving Shewhart Control Chart Performance In The Presence Of Measurement Error Using Multiple Measurements And Two-Stage Sampling, Kenneth W. Linna
Journal of International & Interdisciplinary Business Research
The usual Shewhart control chart efficiently detects large shifts in the mean of a quality characteristic and has been extensively studied in the literature. Most proposed alternatives to the Shewhart chart aim to improve either the signal performance for smaller mean shifts or reduce the sampling effort required to detect a larger shift. Measurement error has been shown in the literature to result in reduced power to detect process shifts. The combination of multiple measurements and two-stage sampling is considered here as a strategy for both regaining power lost due to measurement error and specifically tuning the charts for shifts …
A 3d Characteristics Database Of Land Engraved Areas With Known Subclass, Entni Lin
A 3d Characteristics Database Of Land Engraved Areas With Known Subclass, Entni Lin
Student Theses
Subclass characteristics on bullets may mislead firearm examiners when they rely on traditional 2D images. In order to provide indelible examples for training and help avoid identification errors, 3D topography surface maps and statistical methods of pattern recognition are applied to toolmarks on bullets containing known subclass characteristics. This research was conducted by collecting 3D topography surface map data from land engraved areas of bullets fired through known barrels. This data was processed and used to train the statistical algorithms to predict their origin. The results from the algorithm are compared with the “right answers” (i.e. correct IDs) of the …
Integrating Statistical Methods In Engineering Technology Courses, Sanjeevi Chitikeshi, Jake Hildebrant, Otilia Popescu, Orlando M. Ayala, Vukica M. Jovanovic
Integrating Statistical Methods In Engineering Technology Courses, Sanjeevi Chitikeshi, Jake Hildebrant, Otilia Popescu, Orlando M. Ayala, Vukica M. Jovanovic
Engineering Technology Faculty Publications
Statistical methods and procedures are very important in engineering applications. In most of the engineering fields electronic devices are used as sensing and controlling components. Lack of proper calibration of these devices and of performance analysis using different statistical methods may lead to erroneous measurements and results. In medical or manufacturing areas such errors in the experimental results could be catastrophic. Applying different statistical tests and procedures enhance the quality of engineering work. Traditionally, most engineering curricula have at least one required course in applied statistics in engineering, but that is not generally the case in engineering technology programs. Most …
An Empirical Analysis Of Climatic, Geographic, And Cultural Determinants Of International Tourism, Ethan Straus
An Empirical Analysis Of Climatic, Geographic, And Cultural Determinants Of International Tourism, Ethan Straus
Honors Theses
Each year, billions of people visit different countries all around the world. For many of those countries, tourism is their primary industry, leading to millions of jobs and dollars in revenue. It is expected that by 2020 total International Tourism Receipts will reach 2 trillion US dollars annually. Currently, tourism employs an estimated 200 million people around the world. With the continued progression of climate change, the tourism industry is facing a newfound threat. Global temperatures and the seal level are both expected to rise significantly by the end of the century. Additionally, the Intergovernmental Panel on Climate Change has …
Inversion Copulas From Nonlinear State Space Models With An Application To Inflation Forecasting, Michael S. Smith, Worapree Ole Maneesoonthorn
Inversion Copulas From Nonlinear State Space Models With An Application To Inflation Forecasting, Michael S. Smith, Worapree Ole Maneesoonthorn
Michael Stanley Smith
Spatio-Temporal Dynamics Of Atlantic Cod Bycatch In The Maine Lobster Fishery And Its Impacts On Stock Assessment, Robert E. Boenish
Spatio-Temporal Dynamics Of Atlantic Cod Bycatch In The Maine Lobster Fishery And Its Impacts On Stock Assessment, Robert E. Boenish
Electronic Theses and Dissertations
Of the most iconic fish species in the world, the Atlantic cod (Gadus morhua, hereafter, cod) has been a mainstay in the North Atlantic for centuries. While many global fish stocks have received increased pressure with the advent of new, more efficient fishing technology in the mid-20th century, exceptional pressure has been placed on this prized gadoid. Bycatch, or the unintended catch of organisms, is one of the biggest global fisheries issues. Directly resulting from the failed recovery of cod in the GoM, attention has been placed as to possible sources of unaccounted catch. Among the most …
Discrete Ranked Set Sampling, Heng Cui
Discrete Ranked Set Sampling, Heng Cui
Statistical Science Theses and Dissertations
Ranked set sampling (RSS) is an efficient data collection framework compared to simple random sampling (SRS). It is widely used in various application areas such as agriculture, environment, sociology, and medicine, especially in situations where measurement is expensive but ranking is less costly. Most past research in RSS focused on situations where the underlying distribution is continuous. However, it is not unusual to have a discrete data generation mechanism. Estimating statistical functionals are challenging as ties may truly exist in discrete RSS. In this thesis, we started with estimating the cumulative distribution function (CDF) in discrete RSS. We proposed two …