Statistical Modeling Of Count Data With Over-Dispersion Or Zero-Inflation Problems,
2019
Montclair State University
Statistical Modeling Of Count Data With Over-Dispersion Or Zero-Inflation Problems, Chengxin Zhang
Theses, Dissertations and Culminating Projects
In this study, we will analyze a supply retailing company’s data to model the relationship between their customer’s past purchase behavior to predict their future online purchase behavior. The data was divided into time periods from 2016: P1-P6(January 31st to July 30th) and P7(July 31st to August 27th ). Based on customer’s past purchase information from the P1-P6 period, such as money spent, number of cart additions, transactions type, number of unique purchase dates, number of unique purchase skus, number of page views, number browse dates, company size, and number of products purchased, we aim to find if these information …
A Bayesian Framework For Estimating Seismic Wave Arrival Time,
2019
University of Arkansas, Fayetteville
A Bayesian Framework For Estimating Seismic Wave Arrival Time, Hua Zhong
Graduate Theses and Dissertations
Because earthquakes have a large impact on human society, statistical methods for better studying earthquakes are required. One characteristic of earthquakes is the arrival time of seismic waves at a seismic signal sensor. Once we can estimate the earthquake arrival time accurately, the earthquake location can be triangulated, and assistance can be sent to that area correctly. This study presents a Bayesian framework to predict the arrival time of seismic waves with associated uncertainty. We use a change point framework to model the different conditions before and after the seismic wave arrives. To evaluate the performance of the model, we …
Dynamic Sampling Versions Of Popular Spc Charts For Big Data Analysis,
2019
Boise State University
Dynamic Sampling Versions Of Popular Spc Charts For Big Data Analysis, Samuel Anyaso-Samuel
Boise State University Theses and Dissertations
The statistical process control (SPC) chart is an effective tool for the analysis, interpretation, and visualization of data from sequential processes. Commonly used SPC charts such as the Shewhart, CUSUM and EWMA charts are widely implemented in detecting distributional shifts in various processes. With recent scientific and technological advancements, massive amounts of data continue to be generated by production, medical, agricultural and many other industrial processes. Conventional SPC charts have significant drawbacks in monitoring such processes, specifically when the velocity of the data flow is greater than the run time of the monitoring procedure. In the literature, dynamic sampling control …
A Hidden Markov Factor Analysis Framework For Seizure Detection In Epilepsy Patients,
2019
University of Arkansas, Fayetteville
A Hidden Markov Factor Analysis Framework For Seizure Detection In Epilepsy Patients, Mahboubeh Madadi
Graduate Theses and Dissertations
Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to …
Raman And Surface Enhanced Raman Spectroscopy For Forensic Analysis: Case Studies On The Identification Of Illicit Substances And Artist Pigments,
2019
CUNY Graduate Center
Raman And Surface Enhanced Raman Spectroscopy For Forensic Analysis: Case Studies On The Identification Of Illicit Substances And Artist Pigments, Abed Haddad
Dissertations, Theses, and Capstone Projects
Raman spectroscopy is an effective tool for detecting trace amounts of material by fingerprint-like vibrational spectra. At times, the weak intensity of Raman scattering can make it difficult to distinguish trace materials. This shortcoming is addressed by surface‐enhanced Raman spectroscopy (SERS), which produces strong signal enhancements when target compounds are near metal nanoparticles. For the first part of this thesis, the identification of fentanyl and carfentanil, main culprits in the opioid epidemic, was done using normal Raman and the SERS spectroscopy. As an aid in the assignment of the spectral lines, a computational model was built using Density Functional Theory …
A Comparison Of Standard Denoising Methods For Peptide Identification,
2019
East Tennessee State University
A Comparison Of Standard Denoising Methods For Peptide Identification, Skylar Carpenter
Electronic Theses and Dissertations
Peptide identification using tandem mass spectrometry depends on matching the observed spectrum with the theoretical spectrum. The raw data from tandem mass spectrometry, however, is often not optimal because it may contain noise or measurement errors. Denoising this data can improve alignment between observed and theoretical spectra and reduce the number of peaks. The method used by Lewis et. al (2018) uses a combined constant and moving threshold to denoise spectra. We compare the effects of using the standard preprocessing methods baseline removal, wavelet smoothing, and binning on spectra with Lewis et. al’s threshold method. We consider individual methods and …
Advanced Statistics In Arkansas Sports Reporting,
2019
University of Arkansas, Fayetteville
Advanced Statistics In Arkansas Sports Reporting, Andrew Lee Epperson
Graduate Theses and Dissertations
This study seeks to analyze how Arkansas’ sports journalists are adapting to the recent surge in available advanced statistics that are being used by certain national news organizations. Using in-depth qualitative research that includes in-depth interviews with a number of individuals in the print, broadcast, and athletics side of sports coverage, we discover how journalists and coaches use these next-generation analytics, what they fundamentally mean for the evolution of each respective path, and why so few Arkansas reporters and writers use them at the time of this paper’s defense. We see how budgets and deadlines restrict the use of these …
Comparing Elo, Glicko, Irt, And Bayesian Irt Statistical Models For Educational And Gaming Data,
2019
University of Arkansas, Fayetteville
Comparing Elo, Glicko, Irt, And Bayesian Irt Statistical Models For Educational And Gaming Data, Breanna Morrison
Graduate Theses and Dissertations
Statistical models used for estimating skill or ability levels often vary by field, however their underlying mathematical models can be very similar. Differences in the underlying models can be due to the need to accommodate data with different underlying formats and structure. As the models from varying fields increase in complexity, their ability to be applied to different types of data may have the ability to increase. Models that are applied to educational or psychological data have advanced to accommodate a wide range of data formats, including increased estimation accuracy with sparsely populated data matrices. Conversely, the field of online …
Deep Learning, Medical Physics And Cargo Cult Science.,
2019
University of San Francisco
Deep Learning, Medical Physics And Cargo Cult Science., Miguel Romero Phd, Gilmer Valdes Phd, Timothy Solberg Phd, Yannet Interian Phd
Creative Activity and Research Day - CARD
Deep learning algorithms have become widely popular, with considerable success in fields where datasets have hundreds of thousands or million points. As deep learning is increasingly applied to the fields of medical physics and radiation oncology, a reasonable question follows: are these techniques the best approach, given the unique conditions in our field? In this study, we investigate the dependence of dataset size on the performance of deep learning algorithms compared with more traditional radiomics-based methods.
Deep Neural Network Architectures For Music Genre Classification,
2019
University of San Francisco
Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan
Creative Activity and Research Day - CARD
With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for …
The Andersen Likelihood Ratio Test With A Random Split Criterion Lacks Power,
2019
University College of Teacher Education Styria
The Andersen Likelihood Ratio Test With A Random Split Criterion Lacks Power, Georg Krammer
Journal of Modern Applied Statistical Methods
The Andersen LRT uses sample characteristics as split criteria to evaluate Rasch model fit, or theory driven hypothesis testing for a test. The power and Type I error of a random split criterion was evaluated with a simulation study. Results consistently show a random split criterion lacks power.
Weighted Version Of Generalized Inverse Weibull Distribution,
2019
University of Kashmir, Srinagar, India
Weighted Version Of Generalized Inverse Weibull Distribution, Sofi Mudiasir, S. P. Ahmad
Journal of Modern Applied Statistical Methods
Weighted distributions are used in many fields, such as medicine, ecology, and reliability. A weighted version of the generalized inverse Weibull distribution, known as weighted generalized inverse Weibull distribution (WGIWD), is proposed. Basic properties including mode, moments, moment generating function, skewness, kurtosis, and Shannon’s entropy are studied. The usefulness of the new model was demonstrated by applying it to a real-life data set. The WGIWD fits better than its submodels, such as length biased generalized inverse Weibull (LGIW), generalized inverse Weibull (GIW), inverse Weibull (IW) and inverse exponential (IE) distributions.
Calibration Of Measurements,
2019
University of British Columbia
Calibration Of Measurements, Edward Kroc, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
Traditional notions of measurement error typically rely on a strong mean-zero assumption on the expectation of the errors conditional on an unobservable “true score” (classical measurement error) or on the data themselves (Berkson measurement error). Weakly calibrated measurements for an unobservable true quantity are defined based on a weaker mean-zero assumption, giving rise to a measurement model of differential error. Applications show it retains many attractive features of estimation and inference when performing a naive data analysis (i.e. when performing an analysis on the error-prone measurements themselves), and other interesting properties not present in the classical or Berkson cases. Applied …
Estimation Of Mean With Two-Parameter Ratio-Product-Ratio Estimator In Double Sampling Using Ancillary Information Under Non-Response,
2019
Vikram University, Ujjain, India
Estimation Of Mean With Two-Parameter Ratio-Product-Ratio Estimator In Double Sampling Using Ancillary Information Under Non-Response, Surya K. Pal, Housila P. Singh
Journal of Modern Applied Statistical Methods
Ratio-product-ratio estimators with two parameters in double sampling under non-response are considered along with their properties. Practical conditions are obtained in which the suggested estimators are more proficient than other existing estimators. An example is given.
Capturing Heterogeneity Of Covariate Effects In Hidden Subpopulations In The Presence Of Censoring And Large Number Of Covariates,
2019
University of Nevada, Las Vegas
Capturing Heterogeneity Of Covariate Effects In Hidden Subpopulations In The Presence Of Censoring And Large Number Of Covariates, Farhad Shokoohi, Abbas Khalili, Masoud Asgharian, Shili Lin
Mathematical Sciences Faculty Research
The advent of modern technology has led to a surge of high-dimensional data in biology and health sciences such as genomics, epigenomics and medicine. The high-grade serous ovarian cancer (HGS-OvCa) data reported by The Cancer Genome Atlas (TCGA) Research Network is one example. The TCGA and other research groups have analyzed several aspects of these data. Here we study the relationship between Disease Free Time (DFT) after surgery among ovarian cancer patients and their DNA methylation profiles of genomic features. Such studies pose additional challenges beyond the typical big data problem due to population substructure and censoring. Despite the availability …
A More Powerful Unconditional Exact Test Of Homogeneity For 2 × C Contingency Table Analysis,
2019
Chapman University
A More Powerful Unconditional Exact Test Of Homogeneity For 2 × C Contingency Table Analysis, Louis Ehwerhemuepha, Heng Sok, Cyril Rakovski
Mathematics, Physics, and Computer Science Faculty Articles and Research
The classical unconditional exact p-value test can be used to compare two multinomial distributions with small samples. This general hypothesis requires parameter estimation under the null which makes the test severely conservative. Similar property has been observed for Fisher's exact test with Barnard and Boschloo providing distinct adjustments that produce more powerful testing approaches. In this study, we develop a novel adjustment for the conservativeness of the unconditional multinomial exact p-value test that produces nominal type I error rate and increased power in comparison to all alternative approaches. We used a large simulation study to empirically estimate the …
Sensitivity Analyses For Tumor Growth Models,
2019
Western Kentucky University
Sensitivity Analyses For Tumor Growth Models, Ruchini Dilinika Mendis
Masters Theses & Specialist Projects
This study consists of the sensitivity analysis for two previously developed tumor growth models: Gompertz model and quotient model. The two models are considered in both continuous and discrete time. In continuous time, model parameters are estimated using least-square method, while in discrete time, the partial-sum method is used. Moreover, frequentist and Bayesian methods are used to construct confidence intervals and credible intervals for the model parameters. We apply the Markov Chain Monte Carlo (MCMC) techniques with the Random Walk Metropolis algorithm with Non-informative Prior and the Delayed Rejection Adoptive Metropolis (DRAM) algorithm to construct parameters' posterior distributions and then …
Daily And Seasonal Variability Of Offshore Wind Power On The Central California Coast And Statewide Demand,
2019
California Polytechnic State University, San Luis Obispo
Daily And Seasonal Variability Of Offshore Wind Power On The Central California Coast And Statewide Demand, Matthew Douglas Kehrli
Physics
No abstract provided.
Efficient Class Of Estimators For Finite Population Mean Using Auxiliary Information In Two-Occasion Successive Sampling,
2019
Indian Institute of Technology (ISM)
Efficient Class Of Estimators For Finite Population Mean Using Auxiliary Information In Two-Occasion Successive Sampling, G. N. Singh, Mohd Khalid
Journal of Modern Applied Statistical Methods
In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a …
Spatio-Temporal Cluster Detection And Local Moran Statistics Of Point Processes,
2019
Old Dominion University
Spatio-Temporal Cluster Detection And Local Moran Statistics Of Point Processes, Jennifer L. Matthews
Mathematics & Statistics Theses & Dissertations
Moran's index is a statistic that measures spatial dependence, quantifying the degree of dispersion or clustering of point processes and events in some location/area. Recognizing that a single Moran's index may not give a sufficient summary of the spatial autocorrelation measure, a local indicator of spatial association (LISA) has gained popularity. Accordingly, we propose extending LISAs to time after partitioning the area and computing a Moran-type statistic for each subarea. Patterns between the local neighbors are unveiled that would not otherwise be apparent. We consider the measures of Moran statistics while incorporating a time factor under simulated multilevel Palm distribution, …
