Current Search: Nickerson, David (x)
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- Title
- Use of Integrated Training Environments to Sustain Army Warfighting Proficiency in an Era of Constrained Resources: Understanding What's Required to Win the First Battle of the Next Conflict.
- Creator
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Lerz, Edward, Proctor, Michael, Nickerson, David, Goodwin, Gregory, University of Central Florida
- Abstract / Description
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This research investigates the current state and ability of homestation training infrastructure (TADSS, networks, and facilities) and framework for training (scenarios, databases, and training support packages) to support a Live Virtual Constructive (-) Integrating Architecture (LVC-IA) delivered Integrated Training Environment (ITE). As combat operations in Central and Southwest Asia come to a close the Army is faced with extreme post-conflict budget cuts and force reductions. Continued...
Show moreThis research investigates the current state and ability of homestation training infrastructure (TADSS, networks, and facilities) and framework for training (scenarios, databases, and training support packages) to support a Live Virtual Constructive (-) Integrating Architecture (LVC-IA) delivered Integrated Training Environment (ITE). As combat operations in Central and Southwest Asia come to a close the Army is faced with extreme post-conflict budget cuts and force reductions. Continued evolution of Army training methodology is required to overcome limited resources and maintain force readiness in the anticipated (")era of persistent conflict("). A LVC-IA delivered ITE promises to be the next step in the evolution of training. Interoperation of live, virtual, and constructive simulations in a persistent and consistent manner can collectively train brigade and below units on combined arms tasks in a resource constrained homestation environment. However, LVC-IA cannot act alone in establishing the ITE. Prior to the fielding of LVC-IA, local installations must already possess a training infrastructure that optimizes training resources as well as a framework for training that meets Operational Adaptability training requirements. To measure the perceived state and ability of homestation training infrastructure and framework for training to support a LVC-IA delivered ITE, a survey was conducted of homestation training community members at the 18 Army installations scheduled for LVC-IA fielding. Additionally, perceptions regarding the role of LVC-IA in establishing the ITE and emerging resources, useful in the development of local framework for training were sought. Findings, conclusions, limitations, lessons learned, and recommendations for future research are presented.?
Show less - Date Issued
- 2013
- Identifier
- CFE0005104, ucf:50755
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005104
- Title
- STATISTICAL ANALYSIS OF DEPRESSION AND SOCIAL SUPPORT CHANGE IN ARAB IMMIGRANT WOMEN IN USA.
- Creator
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Blbas, Hazhar, Uddin, Nizam, Nickerson, David, Aroian, Karen, University of Central Florida
- Abstract / Description
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Arab Muslim immigrant women encounter many stressors and are at risk for depression. Social supports from husbands, family and friends are generally considered mitigating resources for depression. However, changes in social support over time and the effects of such supports on depression at a future time period have not been fully addressed in the literature This thesis investigated the relationship between demographic characteristics, changes in social support, and depression in Arab Muslim...
Show moreArab Muslim immigrant women encounter many stressors and are at risk for depression. Social supports from husbands, family and friends are generally considered mitigating resources for depression. However, changes in social support over time and the effects of such supports on depression at a future time period have not been fully addressed in the literature This thesis investigated the relationship between demographic characteristics, changes in social support, and depression in Arab Muslim immigrant women to the USA. A sample of 454 married Arab Muslim immigrant women provided demographic data, scores on social support variables and depression at three time periods approximately six months apart. Various statistical techniques at our disposal such as boxplots, response curves, descriptive statistics, ANOVA and ANCOVA, simple and multiple linear regressions have been used to see how various factors and variables are associated with changes in social support from husband, extended family and friend over time. Simple and multiple regression analyses are carried out to see if any variable observed at the time of first survey can be used to predict depression at a future time. Social support from husband and friend, husband's employment status and education, and depression at time one are found to be significantly associated with depression at time three. Finally, logistic regression analysis conducted for a binary depression outcome variable indicated that lower total social support and higher depression score of survey participants at the time of first survey increase their probability of being depressed at the time of third survey.
Show less - Date Issued
- 2014
- Identifier
- CFE0005133, ucf:50676
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005133
- Title
- Accelerated Life Model with Various Types of Censored Data.
- Creator
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Pridemore, Kathryn, Pensky, Marianna, Mikusinski, Piotr, Swanson, Jason, Nickerson, David, University of Central Florida
- Abstract / Description
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The Accelerated Life Model is one of the most commonly used tools in the analysis of survival data which are frequently encountered in medical research and reliability studies. In these types of studies we often deal with complicated data sets for which we cannot observe the complete data set in practical situations due to censoring. Such difficulties are particularly apparent by the fact that there is little work in statistical literature on the Accelerated Life Model for complicated types...
Show moreThe Accelerated Life Model is one of the most commonly used tools in the analysis of survival data which are frequently encountered in medical research and reliability studies. In these types of studies we often deal with complicated data sets for which we cannot observe the complete data set in practical situations due to censoring. Such difficulties are particularly apparent by the fact that there is little work in statistical literature on the Accelerated Life Model for complicated types of censored data sets, such as doubly censored data, interval censored data, and partly interval censored data.In this work, we use the Weighted Empirical Likelihood approach (Ren, 2001) to construct tests, confidence intervals, and goodness-of-fit tests for the Accelerated Life Model in a unified way for various types of censored data. We also provide algorithms for implementation and present relevant simulation results.I began working on this problem with Dr. Jian-Jian Ren. Upon Dr. Ren's departure from the University of Central Florida I completed this dissertation under the supervision of Dr. Marianna Pensky.
Show less - Date Issued
- 2013
- Identifier
- CFE0004913, ucf:49613
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004913
- Title
- Mahalanobis kernel-based support vector data description for detection of large shifts in mean vector.
- Creator
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Nguyen, Vu, Maboudou, Edgard, Nickerson, David, Schott, James, University of Central Florida
- Abstract / Description
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Statistical process control (SPC) applies the science of statistics to various process control in order to provide higher-quality products and better services. The K chart is one among the many important tools that SPC offers. Creation of the K chart is based on Support Vector Data Description (SVDD), a popular data classifier method inspired by Support Vector Machine (SVM). As any methods associated with SVM, SVDD benefits from a wide variety of choices of kernel, which determines the...
Show moreStatistical process control (SPC) applies the science of statistics to various process control in order to provide higher-quality products and better services. The K chart is one among the many important tools that SPC offers. Creation of the K chart is based on Support Vector Data Description (SVDD), a popular data classifier method inspired by Support Vector Machine (SVM). As any methods associated with SVM, SVDD benefits from a wide variety of choices of kernel, which determines the effectiveness of the whole model. Among the most popular choices is the Euclidean distance-based Gaussian kernel, which enables SVDD to obtain a flexible data description, thus enhances its overall predictive capability. This thesis explores an even more robust approach by incorporating the Mahalanobis distance-based kernel (hereinafter referred to as Mahalanobis kernel) to SVDD and compare it with SVDD using the traditional Gaussian kernel. Method's sensitivity is benchmarked by Average Run Lengths obtained from multiple Monte Carlo simulations. Data of such simulations are generated from multivariate normal, multivariate Student's (t), and multivariate gamma populations using R, a popular software environment for statistical computing. One case study is also discussed using a real data set received from Halberg Chronobiology Center. Compared to Gaussian kernel, Mahalanobis kernel makes SVDD and thus the K chart significantly more sensitive to shifts in mean vector, and also in covariance matrix.
Show less - Date Issued
- 2015
- Identifier
- CFE0005676, ucf:50170
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005676
- Title
- Factors limiting native species establishment on former agricultural lands.
- Creator
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Weiler-Lazarz, Annalisa, VonHolle, Mary, Quintana-Ascencio, Pedro, Neill, Christopher, Nickerson, David, University of Central Florida
- Abstract / Description
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Restoration of abandoned, nonnative species-dominated agricultural lands provides opportunities for conserving declining shrubland and grassland ecosystems. Land-use legacies, such as elevated soil fertility and pH from agricultural amendments, often persist for years and can favor nonnative species at the expense of native species. Understanding the factors that limit native species establishment on abandoned agricultural lands can provide important insights for restoration and conservation...
Show moreRestoration of abandoned, nonnative species-dominated agricultural lands provides opportunities for conserving declining shrubland and grassland ecosystems. Land-use legacies, such as elevated soil fertility and pH from agricultural amendments, often persist for years and can favor nonnative species at the expense of native species. Understanding the factors that limit native species establishment on abandoned agricultural lands can provide important insights for restoration and conservation of native species on human-modified lands. I conducted two field experiments on abandoned agricultural lands: a former pasture on Martha's Vineyard, MA and a former citrus grove at Merritt Island National Wildlife Refuge (MINWR) in Titusville, FL. In these experiments I tested how soil chemical properties affect native and nonnative species abundance and how different methods of removing nonnative, invasive species affect native and nonnative species abundance. In the first experiment, specifically I tested how restoration treatments affect competition between existing nonnative agricultural plant species and native plant species that are targets for sandplain grassland restoration on Martha's Vineyard, MA. At MINWR, I examined how lowering soil fertility with carbon additions and lowering soil pH by applying sulfur affects nonnative species richness and cover (in two former citrus groves that were historically scrub/scrubby flatwoods. Overall, I found that biotic factors, such as competition with nonnative species, play a stronger role in limiting native species establishment than soil chemical properties. Likewise, control of nonnative, invasive species is most effective with mechanical treatments to physically reduce cover, rather than altering soil chemical properties.
Show less - Date Issued
- 2012
- Identifier
- CFE0004323, ucf:49462
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004323
- Title
- Model Selection via Racing.
- Creator
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Zhang, Tiantian, Georgiopoulos, Michael, Anagnostopoulos, Georgios, Wu, Annie, Hu, Haiyan, Nickerson, David, University of Central Florida
- Abstract / Description
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Model Selection (MS) is an important aspect of machine learning, as necessitated by the No Free Lunch theorem. Briefly speaking, the task of MS is to identify a subset of models that are optimal in terms of pre-selected optimization criteria. There are many practical applications of MS, such as model parameter tuning, personalized recommendations, A/B testing, etc. Lately, some MS research has focused on trading off exactness of the optimization with somewhat alleviating the computational...
Show moreModel Selection (MS) is an important aspect of machine learning, as necessitated by the No Free Lunch theorem. Briefly speaking, the task of MS is to identify a subset of models that are optimal in terms of pre-selected optimization criteria. There are many practical applications of MS, such as model parameter tuning, personalized recommendations, A/B testing, etc. Lately, some MS research has focused on trading off exactness of the optimization with somewhat alleviating the computational burden entailed. Recent attempts along this line include metaheuristics optimization, local search-based approaches, sequential model-based methods, portfolio algorithm approaches, and multi-armed bandits.Racing Algorithms (RAs) are an active research area in MS, which trade off some computational cost for a reduced, but acceptable likelihood that the models returned are indeed optimal among the given ensemble of models. All existing RAs in the literature are designed as Single-Objective Racing Algorithm (SORA) for Single-Objective Model Selection (SOMS), where a single optimization criterion is considered for measuring the goodness of models. Moreover, they are offline algorithms in which MS occurs before model deployment and the selected models are optimal in terms of their overall average performances on a validation set of problem instances. This work aims to investigate racing approaches along two distinct directions: Extreme Model Selection (EMS) and Multi-Objective Model Selection (MOMS). In EMS, given a problem instance and a limited computational budget shared among all the candidate models, one is interested in maximizing the final solution quality. In such a setting, MS occurs during model comparison in terms of maximum performance and involves no model validation. EMS is a natural framework for many applications. However, EMS problems remain unaddressed by current racing approaches. In this work, the first RA for EMS, named Max-Race, is developed, so that it optimizes the extreme solution quality by automatically allocating the computational resources among an ensemble of problem solvers for a given problem instance. In Max-Race, significant difference between the extreme performances of any pair of models is statistically inferred via a parametric hypothesis test under the Generalized Pareto Distribution (GPD) assumption. Experimental results have confirmed that Max-Race is capable of identifying the best extreme model with high accuracy and low computational cost. Furthermore, in machine learning, as well as in many real-world applications, a variety of MS problems are multi-objective in nature. MS which simultaneously considers multiple optimization criteria is referred to as MOMS. Under this scheme, a set of Pareto optimal models is sought that reflect a variety of compromises between optimization objectives. So far, MOMS problems have received little attention in the relevant literature. Therefore, this work also develops the first Multi-Objective Racing Algorithm (MORA) for a fixed-budget setting, namely S-Race. S-Race addresses MOMS in the proper sense of Pareto optimality. Its key decision mechanism is the non-parametric sign test, which is employed for inferring pairwise dominance relationships. Moreover, S-Race is able to strictly control the overall probability of falsely eliminating any non-dominated models at a user-specified significance level. Additionally, SPRINT-Race, the first MORA for a fixed-confidence setting, is also developed. In SPRINT-Race, pairwise dominance and non-dominance relationships are established via the Sequential Probability Ratio Test with an Indifference zone. Moreover, the overall probability of falsely eliminating any non-dominated models or mistakenly retaining any dominated models is controlled at a prescribed significance level. Extensive experimental analysis has demonstrated the efficiency and advantages of both S-Race and SPRINT-Race in MOMS.
Show less - Date Issued
- 2016
- Identifier
- CFE0006203, ucf:51094
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006203