Physical Sciences and Mathematics Commons^™

Stability Condition Of A Retrial Queueing System With Abandoned And Feedback Customers, Amina A. Bouchentouf, Abbes Rabhi, Lahcene Yahiaoui

Applications and Applied Mathematics: An International Journal (AAM)

This paper deals with the stability of a retrial queueing system with two orbits, abandoned and feedback customers. Two independent Poisson streams of customers arrive to the system, and flow into a single-server service system. An arriving one of type i; i = 1; 2, is handled by the server if it is free; otherwise, it is blocked and routed to a separate type-i retrial (orbit) queue that attempts to re-dispatch its jobs at its specific Poisson rate. The customer in the orbit either attempts service again after a random time or gives up receiving service and leaves the system …

Analysis Of Repairable M[X]/(G1,G2)/1 - Feedback Retrial G-Queue With Balking And Starting Failures Under At Most J Vacations, P. Rajadurai, M. C. Saravanarajan, V. M. Chandrasekaran

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we discuss the steady state analysis of a batch arrival feedback retrial queue with two types of service and negative customers. Any arriving batch of positive customers finds the server is free, one of the customers from the batch enters into the service area and the rest of them join into the orbit. The negative customer, arriving during the service time of a positive customer, will remove the positive customer in-service and the interrupted positive customer either enters into the orbit or leaves the system. If the orbit is empty at the service completion of each type …

An Optimal Reinsurance Contract From Insurer's And Reinsurer's Viewpoints, Ali P. Bazaz, Amir T. Payandeh Najafabadi

Applications and Applied Mathematics: An International Journal (AAM)

This article constructs two classes of appropriate reinsurance contracts from both an insurer’s and a reinsurer’s viewpoints. The first class, say C; has been constructed by minimizing the conditional tail expectation, say CTE, of an insurer’s random risk. Then an optimal reinsurance contract has been obtained by estimating the reinsurance’s random risk, using the Bayesian estimation method while the second class of reinsurance contracts, say C*; is obtained by minimizing a convex combination of the CTE of both the insurer’s and reinsurer’s random risks. These two approaches consider both the insurer’s and reinsurer’s viewpoints to establish an optimal reinsurance contract. …

Calorimetry And Body Composition Research In Broilers And Broiler Breeders, Justina Victoria Caldas Cueva

Graduate Theses and Dissertations

Indirect calorimetry to study heat production (HP) and dual energy X-ray absorptiometry (DEXA) for body composition (BC) are powerful techniques to study the dynamics of energy and protein utilization in poultry. The first two chapters present the BC (dry matter, lean, protein, and fat, bone mineral, calcium and phosphorus) of modern broilers from 1 – 60 d of age analyzed by chemical analysis and DEXA. DEXA has been validated for precision, standardized for position, and equations and validations developed for chickens under two different feeding levels. These equations are unique to the machine and software in use. Research in broilers …

Predicting Intraday Financial Market Dynamics Using Takens' Vectors; Incorporating Causality Testing And Machine Learning Techniques, Abubakar-Sadiq Bouda Abdulai

Electronic Theses and Dissertations

Traditional approaches to predicting financial market dynamics tend to be linear and stationary, whereas financial time series data is increasingly nonlinear and non-stationary. Lately, advances in dynamical systems theory have enabled the extraction of complex dynamics from time series data. These developments include theory of time delay embedding and phase space reconstruction of dynamical systems from a scalar time series. In this thesis, a time delay embedding approach for predicting intraday stock or stock index movement is developed. The approach combines methods of nonlinear time series analysis with those of causality testing, theory of dynamical systems and machine learning (artificial …

Niche-Based Modeling Of Japanese Stiltgrass (Microstegium Vimineum) Using Presence-Only Information, Nathan Bush

Masters Theses

The Connecticut River watershed is experiencing a rapid invasion of aggressive non-native plant species, which threaten watershed function and structure. Volunteer-based monitoring programs such as the University of Massachusetts’ OutSmart Invasives Species Project, Early Detection Distribution Mapping System (EDDMapS) and the Invasive Plant Atlas of New England (IPANE) have gathered valuable invasive plant data. These programs provide a unique opportunity for researchers to model invasive plant species utilizing citizen-sourced data. This study took advantage of these large data sources to model invasive plant distribution and to determine environmental and biophysical predictors that are most influential in dispersion, and to identify …

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

Srinivasa Rao Gadde Dr.

A multicomponent system of k components having strengths following k- independently and identically distributed random variables x1, x2, ..., xk 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 distributions; the reliability estimators are compared asymptotically. Monte-Carlo …

A Localized Approach To The Origins Of Pottery In Upper Mesopotamia, Elizabeth Gibbon

Laurier Undergraduate Journal of the Arts

No abstract provided.

Monitoring For Adverse Events Post Marketing Approval Of Drugs, Karl E. Peace, Macaulay Okwuokenye

Biostatistics Faculty Publications

This brief communication provides information to those developing monitoring plans for serious adverse events (SAE’s) following regulatory approval of a new drug. In addition, we (1) illustrate how many patients would need to be treated in order to have high confidence of seeing at least 1 pre-specified SAE, (2) show that absence of proof of a SAE is not proof of absence of that SAE, and (3) identify statistical methodology that could be used for formal statistical monitoring of SAE’s.

Meta-Analysis Of Genome-Wide Association Studies With Correlated Individuals: Application To The Hispanic Community Health Study/Study Of Latinos (Hchs/Sol), Tamar Sofer, John R. Shaffer, Misa Graff, Qibin Qi, Adrienne M. Stilp, Stephanie M. Gogarten, Kari E. North, Carmen R. Isasi, Cathy C. Laurie, Adam A. Szpiro

UW Biostatistics Working Paper Series

Investigators often meta-analyze multiple genome-wide association studies (GWASs) to increase the power to detect associations of single nucleotide polymorphisms (SNPs) with a trait. Meta-analysis is also performed within a single cohort that is stratified by, e.g., sex or ancestry group. Having correlated individuals among the strata may complicate meta-analyses, limit power, and inflate Type 1 error. For example, in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), sources of correlation include genetic relatedness, shared household, and shared community. We propose a novel mixed-effect model for meta-analysis, “MetaCor", which accounts for correlation between stratum-specific effect estimates. Simulations show that MetaCor controls …

Models Describing The Sea Level Rise In Key West, Florida, Karm-Ervin Jean

FIU Electronic Theses and Dissertations

Lately, we have been noticing an unusual rise in the sea level near many Floridian cities. By 2060, scientists believe that the sea level in the city of Key West will reach between 22.86 to 60.96 centimeters (Strauss et al. 2012). The consequences of sea level rise are unpleasant by gradually tearing away our beaches and natural resources, destroying our homes and businesses, etc. Definitively, a continual increase of the sea level will affect everyone either directly or indirectly.

In this study, the sea level measurements of four Floridian coastal cities (including Key West) are collected in order to describe …

Gis-Integrated Mathematical Modeling Of Social Phenomena At Macro- And Micro- Levels—A Multivariate Geographically-Weighted Regression Model For Identifying Locations Vulnerable To Hosting Terrorist Safe-Houses: France As Case Study, Elyktra Eisman

FIU Electronic Theses and Dissertations

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to …

Bayes Multiple Binary Classifier - How To Make Decisions Like A Bayesian, Wensong Wu

Mathematics Colloquium Series

This presentation will start by a general introduction of Bayesian statistics, which has become popular in the era of big data. Then we consider a two-class classification problem, where the goal is to predict the class membership of M units based on the values of high-dimensional categorical predictor variables as well as both the values of predictor variables and the class membership of other N independent units. We focus on applying generalized linear regression models with Boolean expressions of categorical predictors. We consider a Bayesian and decision-theoretic framework, and develop a general form of Bayes multiple binary classification functions with …

Estimation Problems In Complex Field Studies With Deep Interactions: Time-To-Event And Local Regression Models For Environmental Effects On Vital Rates, Krzysztof M. Sakrejda

Doctoral Dissertations

Field studies that measure vital rates in context over extended time periods are a cornerstone of our understanding of population processes. These studies inform us about the relationship between biological process and environmental noise in an irreplaceable way. These data sets bring ``big data'' and ``big model'' challenges, which limit the application of standard software (e.g., \textbf{BUGS}). The environmental sensitivity of vital rates is also expected to exhibit interactions and non-linearity, which typically result in difficult model selection questions in large data sets. Finally, long-term ecological data sets often contain complex temporal structure. In commonly applied discrete-time models complex temporal …

Wind Power Capacity Value Metrics And Variability: A Study In New England, Frederick W. Letson

Doctoral Dissertations

Capacity value is the contribution of a power plant to the ability of the power system to meet high demand. As wind power penetration in New England, and worldwide, increases so does the importance of identifying the capacity contribution made by wind power plants. It is critical to accurately characterize the capacity value of these wind power plants and the variability of the capacity value over the long term. This is important in order to avoid the cost of keeping extra power plants operational while still being able to cover the demand for power reliably. This capacity value calculation is …

Variable Selection In Single Index Varying Coefficient Models With Lasso, Peng Wang

Doctoral Dissertations

Single index varying coefficient model is a very attractive statistical model due to its ability to reduce dimensions and easy-of-interpretation. There are many theoretical studies and practical applications with it, but typically without features of variable selection, and no public software is available for solving it. Here we propose a new algorithm to fit the single index varying coefficient model, and to carry variable selection in the index part with LASSO. The core idea is a two-step scheme which alternates between estimating coefficient functions and selecting-and-estimating the single index. Both in simulation and in application to a Geoscience dataset, we …

Threat Analysis, Countermeaures And Design Strategies For Secure Computation In Nanometer Cmos Regime, Raghavan Kumar

Doctoral Dissertations

Advancements in CMOS technologies have led to an era of Internet Of Things (IOT), where the devices have the ability to communicate with each other apart from their computational power. As more and more sensitive data is processed by embedded devices, the trend towards lightweight and efficient cryptographic primitives has gained significant momentum. Achieving a perfect security in silicon is extremely difficult, as the traditional cryptographic implementations are vulnerable to various active and passive attacks. There is also a threat in the form of "hardware Trojans" inserted into the supply chain by the untrusted third-party manufacturers for economic incentives. Apart …

Physical Activity Classification With Conditional Random Fields, Evan L. Ray

Doctoral Dissertations

In this thesis we develop methods for classifying physical activity using accelerometer recordings. We cast this as a problem of classification in time series with moderate to high dimensional observations at each time point. Specifically, we observe a vector of summary statistics of the accelerometer signal at each point in time, and we wish to use these observations to estimate the type and intensity of physical activity the individual engaged in as it changes over time. Our methods are based on Conditional Random Fields, which allow us to capture temporal dependence in an individual’s physical activity type without requiring us …

Nested Partially-Latent, Class Models For Dependent Binary Data, Estimating Disease Etiology, Zhenke Wu, Maria Deloria-Knoll, Scott L. Zeger

Johns Hopkins University, Dept. of Biostatistics Working Papers

The Pneumonia Etiology Research for Child Health (PERCH) study seeks to use modern measurement technology to infer the causes of pneumonia for which gold-standard evidence is unavailable. The paper describes a latent variable model designed to infer from case-control data the etiology distribution for the population of cases, and for an individual case given his or her measurements. We assume each observation is drawn from a mixture model for which each component represents one cause or disease class. The model addresses a major limitation of the traditional latent class approach by taking account of residual dependence among multivariate binary outcome …

Analysis Of Rheumatoid Arthritis Data Using Logistic Regression And Penalized Approach, Wei Chen

USF Tampa Graduate Theses and Dissertations

In this paper, a rheumatoid arthritis (RA) medicine clinical dataset with an ordinal response is selected to study this new medicine. In the dataset, there are four features, sex, age,treatment, and preliminary. Sex is a binary categorical variable with 1 indicates male, and 0 indicates female. Age is the numerical age of the patients. And treatment is a binary categorical variable with 1 indicates has RA, and 0 indicates does not have RA. And preliminary is a five class categorical variable indicates the patient’s RA severity status before taking the medication. The response Y is 5 class ordinal variable shows …

Ensemble Learning Method On Machine Maintenance Data, Xiaochuang Zhao

USF Tampa Graduate Theses and Dissertations

In the industry, a lot of companies are facing the explosion of big data. With this much information stored, companies want to make sense of the data and use it to help them for better decision making, especially for future prediction. A lot of money can be saved and huge revenue can be generated with the power of big data. When building statistical learning models for prediction, companies in the industry are aiming to build models with efficiency and high accuracy. After the learning models have been developed for production, new data will be generated. With the updated data, the …

A Novel Method For Assessing Co-Monotonicity: An Interplay Between Mathematics And Statistics With Applications, Danang T. Qoyyimi

Electronic Thesis and Dissertation Repository

Numerous problems in econometrics, insurance, reliability engineering, and statistics rely on the assumption that certain functions are monotonic, which may or may not be true in real life scenarios. To satisfy this requirement, from the theoretical point of view, researchers frequently model the underlying phenomena using parametric and semi-parametric families of functions, thus effectively specifying the required shapes of the functions. To tackle these problems in a non-parametric way, when the shape cannot be specified explicitly but only estimated approximately, we suggest indices for measuring the lack of monotonicity in functions. We investigate properties of these indices and offer convenient …

Abcc9/Sur2 In The Brain: Implications For Hippocampal Sclerosis Of Aging And A Potential Therapeutic Target, Peter T. Nelson, Gregory A. Jicha, Wang-Xia Wang, Eseosa T. Ighodaro, Sergey C. Artiushin, Colin G. Nichols, David W. Fardo

Sanders-Brown Center on Aging Faculty Publications

The ABCC9 gene and its polypeptide product, SUR2, are increasingly implicated in human neurologic disease, including prevalent diseases of the aged brain. SUR2 proteins are a component of the ATP-sensitive potassium (“K _ATP ”) channel, a metabolic sensor for stress and/or hypoxia that has been shown to change in aging. The K _ATP channel also helps regulate the neurovascular unit. Most brain cell types express SUR2, including neurons, astrocytes, oligodendrocytes, microglia, vascular smooth muscle, pericytes, and endothelial cells. Thus it is not surprising that ABCC9 gene variants are associated with risk for human brain diseases. For example, Cantu syndrome is …

Approaches For Detection Of Unstable Processes: A Comparative Study, Yerriswamy Wooluru, D. R. Swamy, P. Nagesh

Journal of Modern Applied Statistical Methods

A process is stable only when parameters of the distribution of a process or product characteristic remain same over time. Only a stable process has the ability to perform in a predictable manner over time. Statistical analysis of process data usually assume that data are obtained from stable process. In the absence of control charts, the hypothesis of process stability is usually assessed by visual examination of the pattern in the run chart. In this paper appropriate statistical approaches have been adopted to detect instability in the process and compared their performance with the run chart of considerably shorter length …

Contrails: Causal Inference Using Propensity Scores, Dean S. Barron

Journal of Modern Applied Statistical Methods

Contrails are clouds caused by airplane exhausts, which geologists contend decrease daily temperature ranges on Earth. Following the 2001 World Trade Center attack, cancelled domestic flights triggered the first absence of contrails in decades. Resultant exceptional data capacitated causal inference analysis by propensity score matching. Estimated contrail effect was 6.8981°F.

The Bayes Factor For Case-Control Studies With Misclassified Data, Tzesan Lee

Journal of Modern Applied Statistical Methods

The question of how to test if collected data for a case-control study are misclassified was investigated. A mixed approach was employed to calculate the Bayes factor to assess the validity of the null hypothesis of no-misclassification. A real-world data set on the association between lung cancer and smoking status was used as an example to illustrate the proposed method.

Bayesian Analysis Under Progressively Censored Rayleigh Data, Gyan Prakash

Journal of Modern Applied Statistical Methods

The one-parameter Rayleigh model is considered as an underlying model for evaluating the properties of Bayes estimator under Progressive Type-II right censored data. The One‑Sample Bayes prediction bound length (OSBPBL) is also measured. Based on two different asymmetric loss functions a comparative study presented for Bayes estimation. A simulation study was used to evaluate their comparative properties.

An Empirical Study On Different Ranking Methods For Effective Data Classification, Ilangovan Sangaiah, A. Vincent Antony Kumar, Appavu Balamurugan

Journal of Modern Applied Statistical Methods

Ranking is the attribute selection technique used in the pre-processing phase to emphasize the most relevant attributes which allow models of classification simpler and easy to understand. It is a very important and a central task for information retrieval, such as web search engines, recommendation systems, and advertisement systems. A comparison between eight ranking methods was conducted. Ten different learning algorithms (NaiveBayes, J48, SMO, JRIP, Decision table, RandomForest, Multilayerperceptron, Kstar) were used to test the accuracy. The ranking methods with different supervised learning algorithms give different results for balanced accuracy. It was shown the selection of ranking methods could be …

Two Stage Robust Ridge Method In A Linear Regression Model, Adewale Folaranmi Lukman, Oyedeji Isola Osowole, Kayode Ayinde

Journal of Modern Applied Statistical Methods

Two Stage Robust Ridge Estimators based on robust estimators M, MM, S, LTS are examined in the presence of autocorrelation, multicollinearity and outliers as alternative to Ordinary Least Square Estimator (OLS). The estimator based on S estimator performs better. Mean square error was used as a criterion for examining the performances of these estimators.

Semi-Parametric Non-Proportional Hazard Model With Time Varying Covariate, Kazeem A. Adeleke, Alfred A. Abiodun, R. A. Ipinyomi

Journal of Modern Applied Statistical Methods

The application of survival analysis has extended the importance of statistical methods for time to event data that incorporate time dependent covariates. The Cox proportional hazards model is one such method that is widely used. An extension of the Cox model with time-dependent covariates was adopted when proportionality assumption are violated. The purpose of this study is to validate the model assumption when hazard rate varies with time. This approach is applied to model data on duration of infertility subject to time varying covariate. Validity is assessed by a set of simulation experiments and results indicate that a non proportional …