Statistics and Probability Commons^™

Multiple Ratio Imputation By The Emb Algorithm: Theory And Simulation, Masayoshi Takahashi

Journal of Modern Applied Statistical Methods

Although multiple imputation is the gold standard of treating missing data, single ratio imputation is often used in practice. Based on Monte Carlo simulation, the Expectation-Maximization with Bootstrapping (EMB) algorithm to create multiple ratio imputation is used to fill in the gap between theory and practice.

Jmasm45: A Computer Program For Bayesian D-Optimal Binary Repeated Measurements Designs (Matlab), Haftom Temesgen Abebe, Frans E. S. Tan, Gerard J. P. Van Breukelen, Martijn P. F. Berger

Journal of Modern Applied Statistical Methods

Planners of longitudinal studies of binary responses in applied sciences have not yet benefitted from optimal designs, which have been shown to improve precision of model parameter estimates, due to absence of a computer program. An interactive computer program for Bayesian optimal binary repeated measurements designs is presented for this purpose.

Weighted Distributions: A Brief Review, Perspective And Characterizations, Aamir Saghir, Gholamhossein G. Hamedani, Sadaf Tazeem, Aneeqa Khadim

Mathematics, Statistics and Computer Science Faculty Research and Publications

The weighted distributions are widely used in many fields such as medicine, ecology and reliability, to name a few, for the development of proper statistical models. Weighted distributions are milestone for efficient modeling of statistical data and prediction when the standard distributions are not appropriate. A good deal of studies related to the weight distributions have been published in the literature. In this article, a brief review of these distributions is carried out. Implications of the differing weight models for future research as well as some possible strategies are discussed. Finally, characterizations of these distributions based on a simple relationship …

Joint Modelling Of Longitudinal Measurements And Time-To-Event Data : Application To Hiv Study, Mirna Walid Halawani

Theses, Dissertations and Culminating Projects

Longitudinal and survival data are frequently collected in biomedical studies. The research questions of interest in these studies often require separate analysis of the outcomes. But in many occasions interest also lies in studying their association structures, such as in biomarker research, where the clinical studies are designed to identify biomarkers with strong prognostic capabilities for event time outcomes. In the separate analyses, a linear mixed-effects model is used for modeling the longitudinal data to study the changing trend of the response overtime when controlling some covariates and a survival model is used to model the time-to-event data. A common …

A Bayesian Variable Selection Method With Applications To Spatial Data, Xiahan Tang

Graduate Theses and Dissertations

This thesis first describes the general idea behind Bayes Inference, various sampling methods based on Bayes theorem and many examples. Then a Bayes approach to model selection, called Stochastic Search Variable Selection (SSVS) is discussed. It was originally proposed by George and McCulloch (1993). In a normal regression model where the number of covariates is large, only a small subset tend to be significant most of the times. This Bayes procedure specifies a mixture prior for each of the unknown regression coefficient, the mixture prior was originally proposed by Geweke (1996). This mixture prior will be updated as data becomes …

An Extended Weighted Exponential Distribution, Abbas Mahdavi, Leila Jabari

Journal of Modern Applied Statistical Methods

A new class of weighted distributions is proposed by incorporating an extended exponential distribution in Azzalini’s (1985) method. Several statistics and reliability properties of this new class of distribution are obtained. Maximum likelihood estimators of the unknown parameters cannot be obtained in explicit forms; they have to be obtained by solving some numerical methods. Two data sets are analyzed for illustrative purposes, and show that the proposed model can be used effectively in analyzing real data.

A Comparison Of Depth Functions In Maximal Depth Classification Rules, Olusola Samuel Makinde, Adeyinka Damilare Adewumi

Journal of Modern Applied Statistical Methods

Data depth has been described as alternative to some parametric approaches in analyzing many multivariate data. Many depth functions have emerged over two decades and studied in literature. In this study, a nonparametric approach to classification based on notions of different data depth functions is considered and some properties of these methods are studied. The performance of different depth functions in maximal depth classifiers is investigated using simulation and real data with application to agricultural industry.

The Double Prior Selection For The Parameter Of Exponential Life Time Model Under Type Ii Censoring, Ronak M. Patel, Achyut C. Patel

Journal of Modern Applied Statistical Methods

A comparison of double informative priors assumed for the parameter of exponential life time model is considered. Three different sets of double priors are included, and the results are compared with a forth single prior. The data is Type II censored and Bayes estimators for the parameter and reliability are carried out under a squared error loss function in the cases of the four different sets of prior distributions. The predictive distribution was derived for future failure time and also for the remaining ordered failure times after the first r failure times have been observed. Corresponding Bayes credible equal tail …

Jmasm43: Teereg: Trimmed Elemental Estimation (R), Wei Jiang, Matthew S. Mayo

Journal of Modern Applied Statistical Methods

Trimmed elemental regression is robust to outliers and violations of model assumptions. Its properties and statistical inference were evaluated using bias-corrected and accelerated bootstrap confidence intervals. An R package named TEEReg is developed to compute the trimmed elemental estimates and the corresponding bootstrap confidence intervals. Two examples are provided to demonstrate its usage.

Outlier Impact And Accommodation On Power, Hongjing Liao, Yanju Li, Gordon P. Brooks

Journal of Modern Applied Statistical Methods

The outliers’ influence on power rates in ANOVA and Welch tests at various conditions was examined and compared with the effectiveness of nonparametric methods and Winsorizing in minimizing the impact of outliers. Results showed that, considering both power and Type I error, a nonparametric test is the safest choice to control the inflation of Type I error with a decent sample size and yield relatively high power.

Book Review: Multivariate Statistical Methods, A Primer, C. R. Rao

Journal of Modern Applied Statistical Methods

Multivariate Statistical Methods, A Primer, 4th Ed. Bryan F. J. Manly and Jorge A. Navarro Alberto. NY: Chapman & Hall / CRC Press. 2016. 264 p. ISBN 10: 1498728960 / ISBN 13: 978-1498728966

Jmasm44: Implementing Multiple Ratio Imputation By The Emb Algorithm (R), Masayoshi Takahashi

Journal of Modern Applied Statistical Methods

Although single ratio imputation is often used to deal with missing values in practice, there is a paucity of discussion regarding multiple ratio imputation. Code in the R statistical environment is presented to execute multiple ratio imputation by the Expectation-Maximization with Bootstrapping (EMB) algorithm.

Statistical Learning Methods For Facial Recognition, Mengyi Jia

Arts & Sciences Graduate Student Theses and Dissertations

Facial recognition techniques have become increasingly popular in recent decades. This thesis investigates the performance of several methods applied to two different face databases, under a variety of poses and illumination settings. PCA, LDA and KNN are compared and contrasted in terms of their accuracy and processing time.

Trend And Return Level Of Extreme Snow Events In New York City, Mintaek Lee

Boise State University Theses and Dissertations

A major winter storm brought up to 42 inches of snow in parts of the Mid-Atlantic and Northeast United States for January 22-24, 2016. The blizzard of January 2016 impacted about 102.8 million people, where at least 55 people died due to the snowstorm and it caused economic losses in a range of $500 million to $3 billion. This thesis studies two important aspects of extreme snow events: maximum snowfall and maximum snow depth. We apply extreme value methods to extreme snowfall and snow depth data from the New York City area to examine if there are any significant linear …

Demographics, Patterns Of Care, And Survival In Pediatric Medulloblastoma, Emily V. Dressler, Therese A. Dolecek, Meng Liu, John L. Villano

Internal Medicine Faculty Publications

We evaluated the American College of Surgeon’s National Cancer Data Base (NCDB) to describe current hospital-based epidemiologic frequency, survival, and patterns of care of pediatric medulloblastoma. We analyzed NCDB 1998–2011 data on medulloblastoma for children ages 0–19 years using logistic and poisson regression, Kaplan–Meier survival estimates, and Cox proportional hazards models. 3647 cases of medulloblastoma in those aged 0–19 years were identified. Chemotherapy was received by 79 and 74% received radiation, with 65% receiving both therapies. Those who received radiation were more likely to be older than four, while those who received chemotherapy were more likely to be age four …

Tuberculosis And Risk Of Acute Myocardial Infarction: A Propensity Score-Matched Analysis, Moises A. Huaman, Richard J. Kryscio, Carl J. Fichtenbaum, David Henson, Elizabeth G. Salt, Timothy R. Sterling, Beth A. Garvy

Biostatistics Faculty Publications

Several pathogens have been associated with increased cardiovascular disease (CVD) risk. Whether this occurs with Mycobacterium tuberculosis infection is unclear. We assessed if tuberculosis disease increased the risk of acute myocardial infarction (AMI). We identified patients with tuberculosis index claims from a large de-identified database of ~15 million adults enrolled in a U.S. commercial insurance policy between 2008 and 2010. Tuberculosis patients were 1:1 matched to patients without tuberculosis claims using propensity scores. We compared the occurrence of index AMI claims between the tuberculosis and non-tuberculosis cohorts using Kaplan–Meier curves and Cox Proportional Hazard models. Data on 2026 patients with …

Novel Statistical Approaches For Missing Values In Truncated High-Dimensional Metabolomics Data With A Detection Threshold., Jasmit Sureshkumar Shah

Electronic Theses and Dissertations

Despite considerable advances in high throughput technology over the last decade, new challenges have emerged related to the analysis, interpretation, and integration of high-dimensional data. The arrival of omics datasets has contributed to the rapid improvement of systems biology, which seeks the understanding of complex biological systems. Metabolomics is an emerging omics field, where mass spectrometry technologies generate high dimensional datasets. As advances in this area are progressing, the need for better analysis methods to provide correct and adequate results are required. While in other omics sectors such as genomics or proteomics there has and continues to be critical understanding …

Eit Imaging Of Admittivities With A D-Bar Method And Spatial Prior: Experimental Results For Absolute And Difference Imaging, Sarah J. Hamilton

Mathematics, Statistics and Computer Science Faculty Research and Publications

Electrical impedance tomography (EIT) is an emerging imaging modality that uses harmless electrical measurements taken on electrodes at a body's surface to recover information about the internal electrical conductivity and or permittivity. The image reconstruction task of EIT is a highly nonlinear inverse problem that is sensitive to noise and modeling errors making the image reconstruction task challenging. D-bar methods solve the nonlinear problem directly, bypassing the need for detailed and time-intensive forward models, to provide absolute (static) as well as time-difference EIT images. Coupling the D-bar methodology with the inclusion of high confidence a priori data results in a …

Spatiotemporal Analyses Of Recycled Water Production, Jana E. Archer

Electronic Theses and Dissertations

Increased demands on water supplies caused by population expansion, saltwater intrusion, and drought have led to water shortages which may be addressed by use of recycled water as recycled water products. Study I investigated recycled water production in Florida and California during 2009 to detect gaps in distribution and identify areas for expansion. Gaps were detected along the panhandle and Miami, Florida, as well as the northern and southwestern regions in California. Study II examined gaps in distribution, identified temporal change, and located areas for expansion for Florida in 2009 and 2015. Production increased in the northern and southern regions …

Assessing The Relationship Between Change Blindness And The Anchoring Effect, Melissa Schoenlein

Honors Projects

A lack of consideration for all aspects of a question prompts fragmented decision making. These decisions, as they leave out fundamental information, repeatedly then lead to a potentially problematic reaction to the target question or stimuli. The anchoring heuristic propels one to make a decision, usually an estimate, based on a presented “fact”, often ignoring additional background and environmental clues. Reducing the rate of occurrence of the anchoring bias is thought to lead to an increase in holistic decision making. To promote this reduction, the purpose of this research was to affirmexamine the relationship between the susceptibility to change blindness …

Peptide Identification: Refining A Bayesian Stochastic Model, Theophilus Barnabas Kobina Acquah

Electronic Theses and Dissertations

Notwithstanding the challenges associated with different methods of peptide identification, other methods have been explored over the years. The complexity, size and computational challenges of peptide-based data sets calls for more intrusion into this sphere. By relying on the prior information about the average relative abundances of bond cleavages and the prior probability of any specific amino acid sequence, we refine an already developed Bayesian approach in identifying peptides. The likelihood function is improved by adding additional ions to the model and its size is driven by two overall goodness of fit measures. In the face of the complexities associated …

Performance Of Imputation Algorithms On Artificially Produced Missing At Random Data, Tobias O. Oketch

Electronic Theses and Dissertations

Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples. Hence, population estimates, and model parameters estimated from such data are likely to be biased.

However, the missing data problem is an area under study, and alternative better statistical procedures have been presented to mitigate its shortcomings. In this paper, we review causes of missing data, and various methods of handling missing data. Our main focus is evaluating various multiple imputation (MI) methods from the multiple imputation of chained equation (MICE) package in the statistical software …

Denoising Tandem Mass Spectrometry Data, Felix Offei

Electronic Theses and Dissertations

Protein identification using tandem mass spectrometry (MS/MS) has proven to be an effective way to identify proteins in a biological sample. An observed spectrum is constructed from the data produced by the tandem mass spectrometer. A protein can be identified if the observed spectrum aligns with the theoretical spectrum. However, data generated by the tandem mass spectrometer are affected by errors thus making protein identification challenging in the field of proteomics. Some of these errors include wrong calibration of the instrument, instrument distortion and noise. In this thesis, we present a pre-processing method, which focuses on the removal of noisy …

A Comparison Of Statistical Methods Relating Pairwise Distance To A Binary Subject-Level Covariate, Rachael Stone

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

A community ecologist provided a motivating data set involving a certain animal species with two behavior groups, along with a pairwise genetic distance matrix among individuals. Many community ecologists have analyzed similar data sets with a method known as the Hopkins method, testing for an association between the subject-level covariate (behavior group) and the pairwise distance. This community ecologist wanted to know if they used the Hopkins method, would their results be meaningful? Their question inspired this thesis work, where a different data set was used for confidentiality reasons. Multiple methods (Hopkins method, ADONIS, ANOSIM, and Distance Regression) were used …

Statistical Methods For Two Problems In Cancer Research: Analysis Of Rna-Seq Data From Archival Samples And Characterization Of Onset Of Multiple Primary Cancers, Jialu Li

Dissertations and Theses (Open Access)

My dissertation is focused on quantitative methodology development and application for two important topics in translational and clinical cancer research.

The first topic was motivated by the challenge of applying transcriptome sequencing (RNA-seq) to formalin-fixation and paraffin-embedding (FFPE) tumor samples for reliable diagnostic development. We designed a biospecimen study to directly compare gene expression results from different protocols to prepare libraries for RNA-seq from human breast cancer tissues, with randomization to fresh-frozen (FF) or FFPE conditions. To comprehensively evaluate the FFPE RNA-seq data quality for expression profiling, we developed multiple computational methods for assessment, such as the uniformity and continuity …

Detecting And Evaluating Therapy Induced Changes In Radiomics Features Measured From Non-Small Cell Lung Cancer To Predict Patient Outcomes, Xenia J. Fave

Dissertations and Theses (Open Access)

The purpose of this study was to investigate whether radiomics features measured from weekly 4-dimensional computed tomography (4DCT) images of non-small cell lung cancers (NSCLC) change during treatment and if those changes are prognostic for patient outcomes or dependent on treatment modality. Radiomics features are quantitative metrics designed to evaluate tumor heterogeneity from routine medical imaging. Features that are prognostic for patient outcome could be used to monitor tumor response and identify high-risk patients for adaptive treatment. This would be especially valuable for NSCLC due to the high prevalence and mortality of this disease.

A novel process was designed to …

Telephone Polls And Pps Sampling: A Potential Boon To The Polling Industry, Jade Mckay Burt

Undergraduate Honors Capstone Projects

In the wake of the 2016 election, the polling industry has no shortage of critics. While these are difficult times for the industry as a whole, there are exciting innovations happening that will serve to benefit and revitalize the industry for years. One of these exciting innovations is Probability Proportional to Size (PPS) sampling. I will elaborate on what PPS sampling is and provide a mathematical foundation for its use in polling. I also discuss what some of the myriad of issues plaguing the polling industry are and then show how PPS sampling can be used to remedy many of …

A Distribution Of The First Order Statistic When The Sample Size Is Random, Vincent Z. Forgo Mr

Electronic Theses and Dissertations

Statistical distributions also known as probability distributions are used to model a random experiment. Probability distributions consist of probability density functions (pdf) and cumulative density functions (cdf). Probability distributions are widely used in the area of engineering, actuarial science, computer science, biological science, physics, and other applicable areas of study. Statistics are used to draw conclusions about the population through probability models. Sample statistics such as the minimum, first quartile, median, third quartile, and maximum, referred to as the five-number summary, are examples of order statistics. The minimum and maximum observations are important in extreme value theory. This paper will …

Bayesian Flexible Modeling Of Interval-Censored Failure Time Data, Sheng-Yang Wang

Theses and Dissertations

Interval-censored data are a special type of survival data, in which the survival time is not accurately observed but known to fall within a specific time interval. Interval censored data commonly arise in real-life epidemiological and medical studies that involve periodic examinations. In this dissertation, several semi-parametric regression models are investigated to provide flexible modeling and robust inference for interval censored data from Bayesian perspectives.

Chapter 1 provides a detailed description about interval-censored data and gives several examples. Existing models and methods for analyzing such interval-censored data are reviewed as well. Chapter 2 develops a unified Bayesian estimation approach under …

Topics In Group Testing With Multiple Infections, Peijie Hou

Theses and Dissertations

Group testing, dating back to the early 1940s, was first proposed to screen for syphilis among US inductees during World War II (Dorfman, 1943). Since then, the benefits of reducing testing costs by employing group testing have been demonstrated in many areas, such as drug discovery, genetics, and infectious disease testing. Traditionally, statistical research in group testing has largely been motivated by applications involving a single infection. With the recent development of multiplex assays that can diagnose multiple infections simultaneously, generalizing the existing group testing literature to incorporate multiple infections is a natural and necessary next step. This dissertation consists …