Indiana University-Purdue University Indianapolis (IUPUI) / Various methods exist to measure physical activity (PA). Subjective methods, such as diaries and surveys are relatively inexpensive ways of measuring one’s PA; how ever, they are riddled with measurement error and bias due to self-report. Wearable accelerometers offer a noninvasive and objective measure of subjects’ PA and are now widely used in observational and clinical studies. Accelerometers record high frequency data and produce an unlabeled time series at the sub-second level. An important activity to identify from such data is walking, since it is often the only form of exercise for certain populations. While much work has been done to advance the use of accelerometers in public health research, methodology is needed for quan tifying the physical characteristics of different types of PA from the raw signal. In my dissertation, I advance the accelerometry research methodology in a three-paper sequence. The first paper is a novel application of functional linear models to model the physical characteristics of walking. We emphasize the signal processing used to prepare the data for analyses, and we apply the methods to a motivating dataset collected in an elder population. The second paper addresses the classification of PA. We designed an experiment and collected the data with the purpose of extracting useful and interpretable features for differentiating among walking, descending stairs, and ascending stairs. We build subject-specific classification models utilizing a tree based classifier. We evaluate the effects of sensor location and tuning parameters on the classification rate of these models. The third paper addresses the classification of walking types at the population level. We propose a robust normalization of features extracted for each subject and compare the model classification results to evaluate the effect of feature normalization. In summary, this work provides a framework for better use of accelerometers in the study of physical activity. / 2 years

Climate changes in temperature and precipitation are already occurring and are projected to further exhibit increasing temperature and precipitation extremes and increasing variation. Such increased temperature variation and decreased precipitation are likely to have a profound impact on vegetation communities, particularly in regions that are dominated by extreme temperatures and strongly seasonal precipitation events. Both temperature and precipitation are tightly linked to vegetation growth and distribution, and in regions such as the U.S. desert southwest, there are a number of rare and endangered species that have a particularly tight knit relationship with their environment. Here, I examine the relationship between these ecohydrological drivers and a specific, little- researched cactus: the Pima Pineapple Cactus (Coryphantha scheeri var. robustispina). C. scheeri is a small, hemispherical cactus that resides in the Santa Cruz and Altar Valleys of Southern Arizona, and very little is known about the conditions that promote C. scheeri distribution and growth. To provide information that may aide in managing this species, I investigate aspects of the distribution and the phenology of this species. With respect to distribution, I hypothesize that (H1) C. scheeri locations are associated with spatial physical and climatic data within its geographic limits. A framework describing the climatic associations of C. scheeri would enable species managers to take advantage of suitable habitat when opportunities arise. With respect to phenology, within established C. scheeri habitat we lack a clear understanding of the impact ecohydrological factors can have on reproduction and size. Therefore, I also hypothesize (H2) that C. scheeri flowering phenology is triggered by available moisture, which may be in the form of precipitation, humidity, or soil moisture. My results indicate that through the use of the classification tree, C. scheeri habitat is strongly associated with climatic and physical variables at a state-wide scale; these associations indicate large losses of suitable habitat under future projected climate scenarios. Additionally, I find that C. scheeri flowering phenology appears to be associated with precipitation and the resulting increase of soil moisture; the data are also suggestive that bud formation might be associated with water-year growing degree day. Because the results indicate a tight coupling with climatic variables, with most suitable habitat within the current range in Arizona projected to be lost under future climate, I suggest managers may be inclined to increase monitoring C. scheeri in an ecohydrological context relative to the variables identified here and to consider conditions and locations where supplemental watering or microclimate amelioration could be beneficial for the species.

Objective: To develop a predictive model for preoperative differentiation between benign (B) and malignant (M) histology in patients with renal masses (RM) using recursive partitioning. Methods: We analyzed preoperative patient and tumour characteristics in 395 subjects who had surgery for RM suspicious for renal cell carcinoma. Results: The model predicted B vs. M histology with an overall accuracy of 89.6% (95% CI 86.2,92.5). It assigned patients with smaller tumours (<5.67cc) and a predominantly (>45%) exophytic component a high risk of B disease (52.6%). Patients with symptoms, larger tumours (>5.67cc) and larger endophytic component (>35%) have a 0% risk of B disease. Conclusion: B vs. M disease can be predicted accurately. This predictive accuracy is higher than that shown in renal biopsy series. It is hypothesized that for smaller and exophytic RMs, a biopsy is indicated. Symptomatic, larger and endophytic RMs should be removed without further investigations.

Worldwide diarrheal disease is a leading cause of morbidity and mortality in children less than five years of age. Incidence and disease severity remain the highest in sub-Saharan Africa. Kenya has an estimated 400,000 severe diarrhea episodes and 9,500 diarrhea-related deaths per year in children. Current statistical methods for estimating etiological and exposure risk factors for moderate-to-severe diarrhea (MSD) in children are constrained by the inability to assess a large number of parameters due to limitations of sample size, complex relationships, correlated predictors, and model assumptions of linearity. This dissertation examines machine learning statistical methods to address weaknesses associated with using traditional logistic regression models. The studies presented here investigate data from a 4-year, prospective, matched case-control study of MSD among children less than five years of age in rural Kenya from the Global Enteric Multicenter Study. The three machine learning approaches were used to examine associations with MSD and include: least absolute shrinkage and selection operator, classification trees, and random forest. A principal finding in all three studies was that machine learning methodological approaches are useful and feasible to implement in epidemiological studies. All provided additional information and understanding of the data beyond using only logistic regression models. The results from all three machine learning approaches were supported by comparable logistic regression results indicating their usefulness as epidemiological tools. This dissertation offers an exploration of methodological alternatives that should be considered more frequently in diarrheal disease epidemiology, and in public health in general.

Approved for public release, distribution is unlimited / The objective of this thesis is to estimate and explain the effects of graduate education and other factors on promotion to the rank of Lieutenant Colonel (O-5) in the US Army. Our focus was primarily on determining whether graduate education provides officers with higher promotion probabilities. Besides graduate education, data that were analyzed include basic demographic traits, the officers' prior enlisted status, and their commissioning source information. The data used in this study were taken from the Active Duty Military Master File for fiscal years 1981 through 2001. This study develops multivariate logit regression and classification tree models to examine and explore the structure of the data sets. Both the regression models and the classification trees yielded positive results for the effect of graduate education on promotion. According to the regression model results, the odds ratio associated with graduate education is between 1.79 and 2.25. Military Academy and ROTC/Scholarship graduates have higher promotion probabilities than those from other sources, and married officers have higher rates than single officers. Additionally, age has a negative effect on promotion; that is, promotion probability decreases with age. Prior enlisted status, number of dependents, gender, race, and DOD primary occupation code do not seem to have statistically significant effects on promotion. / First Lieutenant, Turkish Army

Shifting perspectives on restoration and management of public lands in the inland West have resulted in an increased need for maps of potential natural vegetation which cover large areas at sufficient scale to delineate individual stands . In this study, classification tree modeling was used to predictively model and map the plant association types of a relatively undisturbed wilderness area in the Blue Mountains of northeastern Oregon. Models were developed using field data and data derived from a geographic information system database. Elevation, slope, aspect, annual precipitation, solar radiation, soil type, and topographic position were important predictor variables. The model predicted plant association types with a relatively high degree of accuracy for most plant association types, with the lowest accuracy for the types within the grand fir series. Fuzzy confusion analysis was used to analyze model performance, and indicated the overall model accuracy was 72%.

This study integrated remotely sensed data, geographic information system (GIS), and classification tree-based modeling to delineate ecological units for the Ashley National Forest. Data points , provided by the Ashley National Forest, with a known location and dominant vegetation type, were related to data layers that were determined to be helpful in a landtype classification. These layers included elevation, slope, aspect, potential solar irradiation, precipitation, geology, basins, Landsat thematic mapper (TM) bands 3, 4, 5, and 6, and basic land cover. These points, with their related information, were then used to train the tree-based model for landtype classification. This resulted in a set of rules, in the form of a binary decision tree, that could be applied to the entire study area. After the landtype classification was obtained, it was cross-classified with geology to produce a landtype association layer. This resulting data layer was compared to an existing landtype association map and it was determined, by cross-tabulation, that the two classifications identified many of the same patterns.

Process monitoring in the chemical and other process industries has been of great practical importance. Early detection of faults is critical in avoiding product quality deterioration, equipment damage, and personal injury. The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes, their applications have been difficult. The first part of this work tackles this problem by employing a polynomial-based data preprocessing step that greatly reduces the dimensionality of the neural network process model. The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients, which are usually of far lower dimensionality than the original data, are used to build a neural network model to produce residuals for fault classification. Case studies show a significant reduction in neural model construction time and sometimes better classification results as well. The second part of this research investigates classification trees as a promising approach to fault detection and classification. It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems, and construction time can excessive for high dimensional problems. Fisher Discriminant Analysis (FDA), which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores, is used as a dimensionality reduction tool. Classification trees use the scores to separate observations into different fault classes. A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order. Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-09-04T21:45:51Z No. of bitstreams: 1 PriscillaValessaDeCastroAndrade_DISSERT.pdf: 1841839 bytes, checksum: 3fb757eaa049425550138768d7d96f9b (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-09-12T19:30:23Z (GMT) No. of bitstreams: 1 PriscillaValessaDeCastroAndrade_DISSERT.pdf: 1841839 bytes, checksum: 3fb757eaa049425550138768d7d96f9b (MD5) / Made available in DSpace on 2017-09-12T19:30:23Z (GMT). No. of bitstreams: 1 PriscillaValessaDeCastroAndrade_DISSERT.pdf: 1841839 bytes, checksum: 3fb757eaa049425550138768d7d96f9b (MD5) Previous issue date: 2017-04-28 / De acordo com as mudan?as ambientais, os indiv?duos apresentam diferentes estrat?gias para lidar com os variados est?mulos externos. Os diferentes comportamentos compreendem os diferentes fen?tipos que comp?em uma popula??o. Essas diferen?as podem ser explicadas por altera??es end?genas, como a secre??o hormonal. Por exemplo, os horm?nios modulam comportamentos reprodutivos e processos cognitivos. Com o objetivo de caracterizar as diferen?as individuais em uma popula??o, o presente estudo teve como objetivo testar a rela??o entre os perfis comportamental e hormonal em um grupo de machos lutando peixes, Betta splendens. Um grupo de 86 machos foi observado para constru??o de ninho de bolha, exposi??es agon?sticas em competi??es coespec?ficas e desempenho em um protocolo de aprendizagem espacial. Depois disso, mediram-se os n?veis plasm?ticos de cortisol e testosterona. Um procedimento estat?stico inovador e elegante foi aplicado ao conjunto de dados para separar animais em grupos relacionados ao seu comportamento de constru??o de ninhos (teste de m?dias de k) e depois mostrar quais os par?metros comportamentais e fisiol?gicos que melhor explicam os perfis dos grupos (Random Forest and Classification Tree). Nossos resultados apontam para tr?s perfis distintos: construtores de ninhos (ninhos de 30,74 ? 9,84 cm?), intermedi?rios (ninhos de 13,57 ? 4,23 cm?) e n?o-construtores (ninhos de 2,17 ? 2,25 cm?). Estes grupos apresentaram diferen?as nos comportamentos agon?stico e de aprendizagem, bem como nos n?veis hormonais. O cortisol foi o principal preditor apontado pelo teste Random Forest para a separa??o de indiv?duos nos diferentes grupos: construtores de ninhos e intermedi?rios apresentaram n?veis mais baixos de cortisol, enquanto os n?o-construtores apresentaram os maiores valores de cortisol basal. O segundo mais importante preditor foi o desempenho de aprendizagem, que separou os animais intermedi?rios dos construtores de ninhos (aqueles que aprenderam mais r?pido), seguidos pelos n?veis basais de testosterona e comportamentos agon?sticos. Enquanto os n?veis de testosterona n?o foram significativos para explicar as diferen?as comportamentais, parece estar relacionado com o perfil de constru??o. Nosso achado mostra que diferentes perfis investem de forma diferente na reprodu??o e que o cortisol influencia negativamente o comportamento e a aprendizagem do nidifica??o. Em resumo, nossos dados sugerem que diferentes perfis em uma popula??o s?o determinados por respostas hormonais e comportamentais, e essas diferen?as conferem flexibilidade ? popula??o, permitindo a presen?a de animais que investem mais na reprodu??o enquanto outros mostram defesa e agress?o como a dominante caracter?stica expressa. / According to environmental changes, the individuals show different strategies to coping with the varied external stimuli. The different responders comprise the different phenotypes that compose a population. These differences can be explained by endogenous changes, such as hormonal secretion. For instance, hormones modulate reproductive behaviors and cognitive processes. In order to characterize individual differences in a population, the present study aimed to testing the relationship between behavioral and hormonal profiles in a group of males Fighting fish, Betta splendens. A group of 86 males were observed for bubble nest construction, agonistic displays in conspecific contests and performance in a spatial learning protocol. After that, cortisol and testosterone plasma levels were measured. An innovative and stylish statistical procedure was applied to the data set in order to separate animal in groups related to its nest building behavior (k-means test) and then shown which behavioral and physiological parameters better explain the groups? profiles (Random forest and Classification tree). Our results point to three distinct profiles: nest builders (nests of 30.74 ? 9.84 cm?), intermediates (nests of 13.57 ? 4.23 cm?) and non-builders (nests of 2.17 ? 2.25 cm?). These groups presented marked different in agonistic and learning behavior, as well as hormone levels. Cortisol was the main predictor prepared by the Random Forest test for the separation of individuals in the different groups: nest builders and intermediates showed lower levels of cortisol while non-builders presented the highest basal cortisol values. The second most important predictor was learning performance, that separated animals from the intermediate from the nest builders (faster learners), followed by basal testosterone levels and agonistic behavior displays. While the testosterone levels were not significant to explain behavioral differences, it seems to be related to the construction profile. Our finding shows that different profiles invest differently in reproduction and that cortisol negatively influences nesting behavior and learning. In summary, our data suggest that different profiles in a population are determined by both hormonal and behavioral responses, and these differences confer flexibility to the population, allowing the presence of animals that invest the most in reproduction while other show defense and aggression as the dominant feature expressed.

The aim of this work is to decide the real usage of logistic regression in the market research tasks respecting the needs of final users of research results. The main argument for the final decision is the comparison of its output to the output of an alternative classification method used in practice -- a classification tree method. The topic is divided into three parts. The first part describes the theoretical framework and approaches linked to logistic regression (chapter 2 and 3). The second part analyses the experience with the usage of logistic regression in Czech market research companies (chapter 4) and the topic is closed by applying the method on real data and comparing the output to the classification tree output (chapter 5 and 6).