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Articles 91 - 106 of 106
Full-Text Articles in Applied Statistics
Embodied Emissions Abatement: A Policy Assessment Using Stochastic Analysis, Adolf Acquaye, Aidan Duffy, Biswajit Basu
Embodied Emissions Abatement: A Policy Assessment Using Stochastic Analysis, Adolf Acquaye, Aidan Duffy, Biswajit Basu
Articles
Policymakers traditionally focus on regulating operational energy use in buildings, ignoring other life cycle components such as embodied energy even though this may account for a significant portion of life cycle emissions. Data relating to embodied energy and emissions in buildings is limited. However, stochastic techniques can be used to estimate the distribution of such emissions from buildings. This helps policymakers identify which instruments are appropriate for achieving emissions reductions. A primary aim of this paper is to demonstrate this approach using a sample of apartment buildings in Ireland. A Monte-Carlo simulation suggests that the average probability distribution of embodied …
Stochastic Hybrid Embodied Co2-Eq Analysis: An Application To The Irish Apartment Building Sector, Adolf Acquaye, Aidan Duffy, Biswajit Basu
Stochastic Hybrid Embodied Co2-Eq Analysis: An Application To The Irish Apartment Building Sector, Adolf Acquaye, Aidan Duffy, Biswajit Basu
Articles
Although embodiedCO2-eq analysis has seen recent developments as evident in the establishment of the ISO14040 and 14044 LCA standards, it is recognized that due to weaknesses in gathering data on product-related emissions,embodiedCO2-eq values are probabilistic. This paper presents a stochastic analysis of hybrid embodied CO2-eq in buildings to account for this weakness in traditional methods and, by way of example, applies it to an Irish construction-sector case study. Using seven apartment buildings, 70,000 results are simulated with Monte Carlo analysis and used to derive probabilistic and cumulative embodied CO2-eq intensity distributions for apartment buildings in Ireland. A Wakeby distribution with …
Counting The Impossible: Sampling And Modeling To Achieve A Large State Homeless Count, Jennifer L. Priestley, Jane Massey
Counting The Impossible: Sampling And Modeling To Achieve A Large State Homeless Count, Jennifer L. Priestley, Jane Massey
Faculty Articles
Objective: Using inferential statistics, we develop estimates of the homeless population of a geographically large and economically diverse state -- Georgia.
Methods: Multiple independent data sources (2000 U.S. Census, the 2006 Georgia County Guide, Georgia Chamber of Commerce) were used to develop Clusters of the 150 Georgia Counties. These clusters were used as "strata" to then execute traified sampling. Homeless counts were conducted within the sample counties, allowing for multiple regression models to be developed to generate predictions of homeless persons by county.
Results: In response to a mandate from the US Department of Housing and Urban Development, the State …
Quantitative Interpretation Of A Genetic Model Of Carcinogenesis Using Computer Simulations, Donghai Dai, Brandon Beck, Xiaofang Wang, Cory Howk, Yi Li
Quantitative Interpretation Of A Genetic Model Of Carcinogenesis Using Computer Simulations, Donghai Dai, Brandon Beck, Xiaofang Wang, Cory Howk, Yi Li
Mathematics and Statistics Faculty Publications
The genetic model of tumorigenesis by Vogelstein et al. (V theory) and the molecular definition of cancer hallmarks by Hanahan and Weinberg (W theory) represent two of the most comprehensive and systemic understandings of cancer. Here, we develop a mathematical model that quantitatively interprets these seminal cancer theories, starting from a set of equations describing the short life cycle of an individual cell in uterine epithelium during tissue regeneration. The process of malignant transformation of an individual cell is followed and the tissue (or tumor) is described as a composite of individual cells in order to quantitatively account for intra-tumor …
Maximal Sensitive Dependence And The Optimal Path To Epidemic Extinction, Eric Forgoston, Simone Bianco, Leah B. Shaw, Ira B. Schwartz
Maximal Sensitive Dependence And The Optimal Path To Epidemic Extinction, Eric Forgoston, Simone Bianco, Leah B. Shaw, Ira B. Schwartz
Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works
Extinction of an epidemic or a species is a rare event that occurs due to a large, rare stochastic fluctuation. Although the extinction process is dynamically unstable, it follows an optimal path that maximizes the probability of extinction. We show that the optimal path is also directly related to the finite-time Lyapunov exponents of the underlying dynamical system in that the optimal path displays maximum sensitivity to initial conditions. We consider several stochastic epidemic models, and examine the extinction process in a dynamical systems framework. Using the dynamics of the finite-time Lyapunov exponents as a constructive tool, we demonstrate that …
Projective-Planar Graphs With No K3,4-Minor, John Maharry, Dan Slilaty
Projective-Planar Graphs With No K3,4-Minor, John Maharry, Dan Slilaty
Mathematics and Statistics Faculty Publications
An exact structure is described to classify the projective‐planar graphs that do not contain a K3, 4‐minor.
Epidemic Spread Of Influenza Viruses: The Impact Of Transient Populations On Disease Dynamics, Karen R. Ríos-Soto, Baojun Song, Carlos Castillo-Chavez
Epidemic Spread Of Influenza Viruses: The Impact Of Transient Populations On Disease Dynamics, Karen R. Ríos-Soto, Baojun Song, Carlos Castillo-Chavez
Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works
The recent H1N1 ("swine u") pandemic and recent H5N1 ("avian u") outbreaks have brought increased attention to the study of the role of animal populations as reservoirs for pathogens that could invade human populations. It is believed that pigs acquired u strains from birds and humans, acting as a mixing vessel in generating new influenza viruses. Assessing the role of animal reservoirs, particularly reservoirs involving highly mobile populations (like migratory birds), on disease dispersal and persistence is of interests to a wide range of researchers including public health experts and evolutionary biologists. This paper studies the interactions between transient and …
The Doubly Inflated Poisson And Related Regression Models, Manasi Sheth-Chandra
The Doubly Inflated Poisson And Related Regression Models, Manasi Sheth-Chandra
Mathematics & Statistics Theses & Dissertations
Most real life count data consists of some values that are more frequent than allowed by the common parametric families of distributions. For data consisting of only excess zeros, in a seminal paper Lambert (1992) introduced Zero-Inflated Poisson (ZIP) model, which is a mixture model that accounts for the inflated zeros. In this thesis, two Doubly Inflated Poisson (DIP) probability models, DIP (p, λ) and DIP ( p1, p2, λ), are discussed for situations where there is another inflated value k > 0 besides the inflated zeros. The distributional properties such as identifiability, moments, and conditional probabilities …
Adjusted Empirical Likelihood Models With Estimating Equations For Accelerated Life Tests, Ni Wang, Jye-Chyi Lu, Di Chen, Paul H. Kvam
Adjusted Empirical Likelihood Models With Estimating Equations For Accelerated Life Tests, Ni Wang, Jye-Chyi Lu, Di Chen, Paul H. Kvam
Department of Math & Statistics Faculty Publications
This article proposes an adjusted empirical likelihood estimation (AMELE) method to model and analyze accelerated life testing data. This approach flexibly and rigorously incorporates distribution assumptions and regression structures by estimating equations within a semiparametric estimation framework. An efficient method is provided to compute the empirical likelihood estimates, and asymptotic properties are studied. Real-life examples and numerical studies demonstrate the advantage of the proposed methodology.
Multi-Cause Degradation Path Model: A Case Study On Rubidium Lamp Degradation, Sun Quan, Paul H. Kvam
Multi-Cause Degradation Path Model: A Case Study On Rubidium Lamp Degradation, Sun Quan, Paul H. Kvam
Department of Math & Statistics Faculty Publications
At the core of satellite rubidium standard clocks is the rubidium lamp, which is a critical piece of equipment in a satellite navigation system. There are many challenges in understanding and improving the reliability of the rubidium lamp, including the extensive lifetime requirement and the dearth of samples available for destructive life tests. Experimenters rely on degradation experiments to assess the lifetime distribution of highly reliable products that seem unlikely to fail under the normal stress conditions, because degradation data can provide extra information about product reliability. Based on recent research on the rubidium lamp, this article presents a multi‐cause …
Adjusted Hazard Rate Estimator Based On A Known Censoring Probability, Ülkü Gürler, Paul H. Kvam
Adjusted Hazard Rate Estimator Based On A Known Censoring Probability, Ülkü Gürler, Paul H. Kvam
Department of Math & Statistics Faculty Publications
In most reliability studies involving censoring, one assumes that censoring probabilities are unknown. We derive a nonparametric estimator for the survival function when information regarding censoring frequency is available. The estimator is constructed by adjusting the Nelson–Aalen estimator to incorporate censoring information. Our results indicate significant improvements can be achieved if available information regarding censoring is used. We compare this model to the Koziol–Green model, which is also based on a form of proportional hazards for the lifetime and censoring distributions. Two examples of survival data help to illustrate the differences in the estimation techniques.
Studies In Sampling Techniques And Time Series Analysis, Florentin Smarandache, Rajesh Singh
Studies In Sampling Techniques And Time Series Analysis, Florentin Smarandache, Rajesh Singh
Branch Mathematics and Statistics Faculty and Staff Publications
This book has been designed for students and researchers who are working in the field of time series analysis and estimation in finite population. There are papers by Rajesh Singh, Florentin Smarandache, Shweta Maurya, Ashish K. Singh, Manoj Kr. Chaudhary, V. K. Singh, Mukesh Kumar and Sachin Malik. First chapter deals with the problem of time series analysis and the rest of four chapters deal with the problems of estimation in finite population. The book is divided in five chapters as follows: Chapter 1. Water pollution is a major global problem. In this chapter, time series analysis is carried out …
Uniform And Partially Uniform Redistribution Rules, Florentin Smarandache, Jean Dezert
Uniform And Partially Uniform Redistribution Rules, Florentin Smarandache, Jean Dezert
Branch Mathematics and Statistics Faculty and Staff Publications
This short paper introduces two new fusion rules for combining quantitative basic belief assignments. These rules although very simple have not been proposed in literature so far and could serve as useful alternatives because of their low computation cost with respect to the recent advanced Proportional Conflict Redistribution rules developed in the DSmT framework.
Some Ratio Type Estimators Under Measurement Errors, Florentin Smarandache, Mukesh Kumar, Rajesh Singh, Ashish K. Singh
Some Ratio Type Estimators Under Measurement Errors, Florentin Smarandache, Mukesh Kumar, Rajesh Singh, Ashish K. Singh
Branch Mathematics and Statistics Faculty and Staff Publications
This article addresses the problem of estimating the population mean using auxiliary information in the presence of measurement errors.
Applying Localized Realized Volatility Modeling To Futures Indices, Luella Fu
Applying Localized Realized Volatility Modeling To Futures Indices, Luella Fu
CMC Senior Theses
This thesis extends the application of the localized realized volatility model created by Ying Chen, Wolfgang Karl Härdle, and Uta Pigorsch to other futures markets, particularly the CAC 40 and the NI 225. The research attempted to replicate results though ultimately, those results were invalidated by procedural difficulties.
Bayesian Semiparametric Generalizations Of Linear Models Using Polya Trees, Angela Schoergendorfer
Bayesian Semiparametric Generalizations Of Linear Models Using Polya Trees, Angela Schoergendorfer
University of Kentucky Doctoral Dissertations
In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions may be defined using Polya trees. This dissertation addresses statistical problems for which the Polya tree idea can be utilized to provide efficient and practical methodological solutions.
One problem considered is the estimation of risks, odds ratios, or other similar measures that are derived by specifying a threshold for an observed continuous variable. It has been previously shown that fitting a linear model to the continuous outcome under the assumption of a logistic error distribution leads to more efficient odds ratio estimates. We will show that deviations …