Digital Commons Network^™

Comparing Dietary Score Associations With Lipoprotein Particle Subclass Profiles: A Cross-Sectional Analysis Of A Middle-To Older-Aged Population, Seán R. Millar, Pilar Navarro, Janas M. Harrington, Nitin Shivappa Mbbs, Mph, Ph.D., James Hébert Scd, Ivan J. Perry, Catherine M. Phillips

Faculty Publications

Background and objectives: Lipoprotein particle concentrations and size are associated with increased risk for atherosclerosis and premature cardiovascular disease. Studies also suggest that certain dietary behaviours may be cardioprotective. Limited comparative data regarding any dietary score/index-lipoprotein particle subclass associations exist. Thus, our objective was to assess relationships between the Dietary Approaches to Stop Hypertension (DASH), Health Eating Index-2015 (HEI-2015), Mediterranean Diet (MD) and Energy-adjusted Dietary Inflammatory Index (E-DII™) scores and plasma lipids and lipoprotein profiles to test the hypothesis that healthier diet (better quality and more anti-inflammatory) would be associated with a more favourable lipoprotein profile.

Materials and methods: This …

Evaluating The Efficiency Of Markov Chain Monte Carlo Algorithms, Thuy Scanlon

Graduate Theses and Dissertations

Markov chain Monte Carlo (MCMC) is a simulation technique that produces a Markov chain designed to converge to a stationary distribution. In Bayesian statistics, MCMC is used to obtain samples from a posterior distribution for inference. To ensure the accuracy of estimates using MCMC samples, the convergence to the stationary distribution of an MCMC algorithm has to be checked. As computation time is a resource, optimizing the efficiency of an MCMC algorithm in terms of effective sample size (ESS) per time unit is an important goal for statisticians. In this paper, we use simulation studies to demonstrate how the Gibbs …

Regression Methods For Group Testing Data, Michael Stutz

Theses and Dissertations

Group testing is an efficient method of disease screening, whereby individual specimens (e.g., blood, urine, etc.) are pooled together and tested as a whole for the presence of disease. A common goal is to use data arising from these testing protocols to better understand the relationship between disease status and potential risk factors (e.g., age, symptom status, etc.). Numerous statistical methodologies have been developed for this purpose, most of which are built within the framework of a generalized linear model. Recent authors have suggested the inadequacy of such regression methods to capture the true functional relationships when nonlinear effects are …

Statistical Modeling For High-Dimensional Compositional Data With Applications To The Human Microbiome, Thy Dao

Graduate Theses and Dissertations

Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology, and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data. This dissertation is motivated by some statistical problems arising in the analysis of compositional data. In particular, we focus on the high-dimensional and over-dispersed setting, where the dimensionality of compositions is greater than the sample size and the dispersion parameter is moderate or large. In …

Pareto Distribution Under Hybrid Censoring: Some Estimation, Gyan Prakash

Journal of Modern Applied Statistical Methods

In the present study, the Pareto model is considered as the model from which observations are to be estimated using a Bayesian approach. Properties of the Bayes estimators for the unknown parameters have studied by using different asymmetric loss functions on hybrid censoring pattern and their risks have compared. The properties of maximum likelihood estimation and approximate confidence length have also been investigated under hybrid censoring. The performances of the procedures are illustrated based on simulated data obtained under the Metropolis-Hastings algorithm and a real data set.

Calibration-Based Estimators Using Different Distance Measures Under Two Auxiliary Variables: A Comparative Study, Piyush Kant Rai, Alka Singh, Muhammad Qasim

Journal of Modern Applied Statistical Methods

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.

Analysis Of Genetic Variants Associated With Levels Of Immune Modulating Proteins For Impact On Alzheimer’S Disease Risk Reveal A Potential Role For Siglec14, Benjamin C. Shaw, Yuriko Katsumata, James F. Simpson, David W. Fardo, Steven Estus

Biostatistics Faculty Publications

Genome-wide association studies (GWAS) have identified immune-related genes as risk factors for Alzheimer’s disease (AD), including TREM2 and CD33, frequently passing a stringent false-discovery rate. These genes either encode or signal through immunomodulatory tyrosine-phosphorylated inhibitory motifs (ITIMs) or activation motifs (ITAMs) and govern processes critical to AD pathology, such as inflammation and amyloid phagocytosis. To investigate whether additional ITIM and ITAM-containing family members may contribute to AD risk and be overlooked due to the stringent multiple testing in GWAS, we combined protein quantitative trait loci (pQTL) data from a recent plasma proteomics study with AD associations in a recent …

Tocilizumab And Covid-19: A Meta-Analysis Of 2120 Patients With Severe Disease And Implications For Clinical Trial Methodologies, Azza Sarfraz, Zouina Sarfraz, Muzna Sarfraz, Hinna Aftab, Zainab Pervaiz

Department of Paediatrics and Child Health

Background/aim: Since the outbreak of the COVID-19, numerous therapies to counteract this severe disease have emerged. The benefits of Tocilizumab for severely infected COVID-19 patients and the methodologies of ongoing clinical trials are explored.
Materials and methods: A systematic search adhering to PRISMA guidelines was conducted in PubMed, Cochrane Central, medRxiv, and bioRxiv using the following keywords: “Tocilizumab,” “Actemra,” “COVID-19.” An additional subsearch was conducted on Clinicaltrials.gov to locate ongoing tocilizumab trials.
Results: A total of 13 studies were included in the meta-analysis comprising 2120 patients. The treatment group had lower mortality compared to the control group (OR = 0.42, …

Bayesian Multivariate Joint Modeling For Skewed-Longitudinal And Time-To-Event Data, Lan Xu

USF Tampa Graduate Theses and Dissertations

In epidemiologic and clinical studies, a relatively large number of biomarkers are repeatedly measured in patients over time, often associated with data on epidemiologic and clinical interest events. So, much attention is focused on developing the specific patterns of the longitudinal measurements, and the associations between those patterns and the time to a certain event, such as heart attack, diagnose of disease, time to transplantation, or death. In the last two decades, the research into joint modeling of longitudinal and time-to-event data has received a tremendous amount of attention.

Numerous researchers have proposed joint modeling approaches for a single longitudinal …

Rattle Detection – An Automotive Case Study, Orla Hartley

International Conference on Lean Six Sigma

This case study showcases the use of statistical tools to develop an objective Squeak and Rattle (S&R) measurement and detection test for End Of Line (EOL) sign off in an automotive manufacturing environment. Audio Induced S&R is an unwanted vibration within the vehicle caused by the sound system, impacting on customer perception of vehicle quality. Testing for S&R in an automotive environment has a key challenge; how to robustly detect a rattle at the EOL and thus prevent plant escapes to the customer. The objective test developed used microphones and analysers in order to replace an e subjective listening test. …

Dpp: Deep Predictor For Price Movement From Candlestick Charts, Chih-Chieh Hung, Ying-Ju (Tessa) Chen

Mathematics Faculty Publications

Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An …

Intermountain West Lichen Dna Reference Library, Brian Colgrove

Undergraduate Honors Theses

Accurate estimates of biodiversity can play crucial roles in monitoring ecological health. BYU’s Lichen Air Quality Biomonitoring program (Wright) represents one of the largest biomonitoring programs in the nation. Recently, DNA metabarcoding approaches have shown promise in streamlining lichen biodiversity inventories. However, to date, lichen diversity of western North America is poorly represented in available DNA reference libraries, preventing biologists and land managers from using DNA barcoding to identify unknown specimens. To solve this problem, I have developed a DNA reference library for over 500 species occurring in the Intermountain West region. Using bioinformatic and statistical tools, I created a …

Robust Lag Weighted Lasso For Time Series Model, Tahir R. Dikheel, Alaa Q. Yaseen

Journal of Modern Applied Statistical Methods

The lag-weighted lasso was introduced to deal with lag effects when identifying the true model in time series. This method depends on weights to reflect both the coefficient size and the lag effects. However, the lag weighted lasso is not robust. To overcome this problem, we propose robust lag weighted lasso methods. Both the simulation study and the real data example show that the proposed methods outperform the other existing methods.

Grizzly Bears Mortalities And The Survival Of The Species, Courtney Swanson

Senior Seminars and Capstones

In this paper we aim to understand what is happening in the grizzly bear population mortalities from the year 2010 to 2020. We are performing Classical and Regression Tree (CART) methods and Correspondence Analysis on data provided by the U.S. Geological Survey (USGS). We found certain variables in the data set to be important through CART methods. Correspondence Analysis then allowed us to compare these variables to determine their relationships and association to one another. Most of the grizzly bear deaths are human caused and mainly over land and resources such as food and habitat. This aligns with some of …

Spatiotemporal Interactions Between Surface Coal Mining And Land Cover And Use Changes, Nikolaos Paraskevis, Aikaterini Servou, Christos Roumpos, Francis Pavloudakis

Journal of Sustainable Mining

Long-term surface mining and land cover and use changes have been evidenced to have a critical relationship. This study conducts trend and correlation analysis by statistical tools to quantitatively evaluate this relationship in the Ptolemais (Northern Greece) coal mining area for the period 1990-2018. Firstly, based on Corine data and satellite images, a relative spatial indicator (RSI) was adopted to describe the mineral land areas. Secondly, land cover and use changes were spatially defined using Corine data and ArcGIS tools. The active mining area was then distinguished by dumping area, using Landsat satellite imagery and mining maps, and finally, mine …

Impact Of Neoantigen Expression And T-Cell Activation On Breast Cancer Survival, Wenjing Li, Amei Amei, Francis Bui, Saba Norouzifar, Lingeng Lu, Zuoheng Wang

Mathematical Sciences Faculty Research

Neoantigens are derived from tumor-specific somatic mutations. Neoantigen-based syn-thesized peptides have been under clinical investigation to boost cancer immunotherapy efficacy. The promising results prompt us to further elucidate the effect of neoantigen expression on patient survival in breast cancer. We applied Kaplan–Meier survival and multivariable Cox regression models to evaluate the effect of neoantigen expression and its interaction with T-cell activation on overall survival in a cohort of 729 breast cancer patients. Pearson’s chi-squared tests were used to assess the relationships between neoantigen expression and clinical pathological variables. Spearman correlation analysis was conducted to identify correlations between neoantigen expression, mutation …

Pairwise Balanced Designs From Cyclic Pbib Designs, D. K. Ghosh, N. R. Desai, Shreya Ghosh

Journal of Modern Applied Statistical Methods

A pairwise balanced designs was constructed using cyclic partially balanced incomplete block designs with either (λ1 – λ2) = 1 or (λ2 – λ1) = 1. This method of construction of Pairwise balanced designs is further generalized to construct it using cyclic partially balanced incomplete block design when |(λ1 – λ2)| = p. The methods of construction of pairwise balanced designs was supported with examples. A table consisting parameters of Cyclic PBIB designs and its corresponding constructed pairwise balanced design is also included.

Generalized Ratio-Cum-Product Estimator For Finite Population Mean Under Two-Phase Sampling Scheme, Gajendra Kumar Vishwakarma, Sayed Mohammed Zeeshan

Journal of Modern Applied Statistical Methods

A method to lower the MSE of a proposed estimator relative to the MSE of the linear regression estimator under two-phase sampling scheme is developed. Estimators are developed to estimate the mean of the variate under study with the help of auxiliary variate (which are unknown but it can be accessed conveniently and economically). The mean square errors equations are obtained for the proposed estimators. In addition, optimal sample sizes are obtained under the given cost function. The comparison study has been done to set up conditions for which developed estimators are more effective than other estimators with novelty. The …

Two Different Classes Of Shrinkage Estimators For The Scale Parameter Of The Rayleigh Distribution, Talha Omer, Zawar Hussain, Muhammad Qasim, Said Farooq Shah, Akbar Ali Khan

Journal of Modern Applied Statistical Methods

Shrinkage estimators are introduced for the scale parameter of the Rayleigh distribution by using two different shrinkage techniques. The mean squared error properties of the proposed estimator have been derived. The comparison of proposed classes of the estimators is made with the respective conventional unbiased estimators by means of mean squared error in the simulation study. Simulation results show that the proposed shrinkage estimators yield smaller mean squared error than the existence of unbiased estimators.

Extending Singh-Maddala Distribution, Mohamed Ali Ahmed

Journal of Modern Applied Statistical Methods

A new distribution, the exponentiated transmuted Singh-Maddala distribution (ETSM), is presented, and three important special distributions are illustrated. Some mathematical properties are obtained, and parameters estimation method is applied using maximum likelihood. Illustrations based on random numbers and a real data set are given.

How To Apply Multiple Imputation In Propensity Score Matching With Partially Observed Confounders: A Simulation Study And Practical Recommendations, Albee Ling, Maria Montez-Rath, Maya Mathur, Kris Kapphahn, Manisha Desai

Journal of Modern Applied Statistical Methods

Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied missing data methods resulted in biased and inefficient estimates, …

A New Right-Skewed Upside Down Bathtub Shaped Heavy-Tailed Distribution And Its Applications, Sandeep Kumar Maurya, Sanjay K. Singh, Umesh Singh

Journal of Modern Applied Statistical Methods

A one parameter right skewed, upside down bathtub type, heavy-tailed distribution is derived. Various statistical properties and maximum likelihood approaches for estimation purpose are studied. Five different real data sets with four different models are considered to illustrate the suitability of the proposed model.

On The Level Of Precision Of A Heterogeneous Transfer Function In A Statistical Neural Network Model, Christopher Godwin Udomboso

Journal of Modern Applied Statistical Methods

A heterogeneous function of the statistical neural network is presented from two transfer functions: symmetric saturated linear and hyperbolic tangent sigmoid. The precision of the derived heterogeneous model over their respective homogeneous forms are established, both at increased sample sizes hidden neurons. Results further show the sensitivity of the heterogeneous model to increase in hidden neurons.

A New Generalized Family Of Distributions For Lifetime Data, Maha A. D. Aldahlan, Mohamed G. Khalil, Ahmed Z. Afify

Journal of Modern Applied Statistical Methods

A new class of continuous distributions called the generalized Burr X-G family is introduced. Some special models of the new family are provided. Some of its mathematical properties including explicit expressions for the quantile and generating functions, ordinary and incomplete moments, order statistics and Rényi entropy are derived. The maximum likelihood is used for estimating the model parameters. The flexibility of the generated family is illustrated by means of two applications to real data sets.

Jmasm 57: Bayesian Survival Analysis Of Lomax Family Models With Stan (R), Mohammed H. A. Abujarad, Athar Ali Khan

Journal of Modern Applied Statistical Methods

An attempt is made to fit three distributions, the Lomax, exponential Lomax, and Weibull Lomax to implement Bayesian methods to analyze Myeloma patients using Stan. This model is applied to a real survival censored data so that all the concepts and computations will be around the same data. A code was developed and improved to implement censored mechanism throughout using rstan. Furthermore, parallel simulation tools are also implemented with an extensive use of rstan.

Vif-Regression Screening Ultrahigh Dimensional Feature Space, Hassan S. Uraibi

Journal of Modern Applied Statistical Methods

Iterative Sure Independent Screening (ISIS) was proposed for the problem of variable selection with ultrahigh dimensional feature space. Unfortunately, the ISIS method transforms the dimensionality of features from ultrahigh to ultra-low and may result in un-reliable inference when the number of important variables particularly is greater than the screening threshold. The proposed method has transformed the ultrahigh dimensionality of features to high dimension space in order to remedy of losing some information by ISIS method. The proposed method is compared with ISIS method by using real data and simulation. The results show this method is more efficient and more reliable …

A Simple Random Sampling Modified Dual To Product Estimator For Estimating Population Mean Using Order Statistics, Sanjay Kumar, Priyanka Chhaparwal

Journal of Modern Applied Statistical Methods

Bandopadhyaya (1980) developed a dual to product estimator using robust modified maximum likelihood estimators (MMLE’s). Their properties were obtained theoretically and supported through simulations studies with generated as well as one real data set. Robustness properties in the presence of outliers and confidence intervals were studied.

Penalized Likelihood Estimation Of Gamma Distributed Response Variable Via Corrected Solution Of Regression Coefficients, Rasaki Olawale Olanrewaju

Journal of Modern Applied Statistical Methods

A Gamma distributed response is subjected to regression penalized likelihood estimations of Least Absolute Shrinkage and Selection Operator (LASSO) and Minimax Concave Penalty via Generalized Linear Models (GLMs). The Gamma related disturbance controls the influence of skewness and spread in the corrected path solutions of the regression coefficients.

Inference For Step-Stress Partially Accelerated Life Test Model With An Adaptive Type-I Progressively Hybrid Censored Data, Showkat Ahmad Lone, Ahmadur Rahman, Tanveer A. Tarray

Journal of Modern Applied Statistical Methods

Consider estimating data of failure times under step-stress partially accelerated life tests based on adaptive Type-I hybrid censoring. The mathematical model related to the lifetime of the test units is assumed to follow Rayleigh distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameter and tampering coefficient. Also, the work is conducted under a traditional Type-I hybrid censoring plan (scheme). A Monte Carlo simulation algorithm is used to evaluate and compare the performances of the estimators of the tempering coefficient and model parameters under both progressively hybrid censoring plans. The comparison is carried out on the basis of …

Data-Driven Analytical Modeling Of Multiple Myeloma Cancer, U.S. Crop Production And Monitoring Process, Lohuwa Mamudu

USF Tampa Graduate Theses and Dissertations

Globally, cancer disease is a major health issue causing a lot of deaths. The duration of time an individual diagnosed with a particular type of cancer survives has become a major area of research concern. The Kaplan Meier and Cox Proportional Hazard (Cox-PH) model have been a traditionally used method for survival analysis of cancer data. These techniques of cancer survival analysis are developed from nonparametric and semi-parametric approaches, respectively, which are not as robust as a parametric approach. In this dissertation, we proposed a new method of cancer survival analysis based on a parametric approach using multiple myeloma cancer …