"We analyze data (length, weight and location) from a study done by the Army Corps of Engineers along the Tennessee River basin in the summer of 1980. The purpose is to predict the probability that a hypothetical channel catfish at a location studied is toxic and contains 5 ppm or more DDT in its filet. We incorporate spatial information and treate it separetely from other covariates. Ultimately, we want to predict the probability that a catfish from the unobserved location is toxic. In a preliminary analysis, we examine the data for observed locations using frequentist logistic regression, Bayesian logistic regression, and Bayesian logistic regression with random effects. Later we develop a parsimonious extension of Bayesian logistic regression and the corresponding Gibbs sampler for that model to increase computational feasibility and reduce model parameters. Furthermore, we develop a Bayesian model to impute data for locations where catfish were not observed. A comparison is made between results obtained fitting the model to only observed data and data with missing values imputed. Lastly, a complete model is presented which imputes data for missing locations and calculates the probability that a catfish from the unobserved location is toxic at once. We conclude that length and weight of the fish have negligible effect on toxicity. Toxicity of these catfish are mostly explained by location and spatial effects. In particular, the probability that a catfish is toxic decreases as one moves further downstream from the source of pollution."

We consider the problem of maximum likelihood estimation of logistic sinusoidal regression models and develop some asymptotic theory including the consistency and joint rates of convergence for the maximum likelihood estimators. The key techniques build upon a synthesis of the results of Walker and Song and Li for the widely studied sinusoidal regression model and on making a connection to a result of Radchenko. Monte Carlo simulations are also presented to demonstrate the finite-sample performance of the estimators

Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo e também para corrigir desvios de suposições. Neste trabalho, descrevemos o uso de transformações nos modelos de regressão logística para dados binários e apresentamos modelos envolvendo parâmetros adicionais de modo a obter um ajuste mais adequado. Posteriormente, analisamos o custo da estimação quando são adicionados parâmetros aos modelos e apresentamos os testes de hipóteses relativos aos parâmetros do modelo de regressão logística de Box-Cox. Finalizando, apresentamos alguns métodos de diagnóstico para avaliar a influência das observações nas estimativas dos parâmetros de transformação da covariável, com aplicação a um conjunto de dados reais. / Binary data models have a lot of utilities in many practical situations. In Regrssion Analisys, transformations can be applied to linearize or simplify the model and correct deviations of the suppositions. In this dissertation, we show the use of the transformations in logistic models to binary data models and models involving additional parameters to obtain more appropriate fits. We also present the cost of the estimation when parameters are added to models, hypothesis tests of the parameters in the Box-Cox logistic regression model and finally, diagnostics methods to evaluate the influence of the observations in the estimation of the transformation covariate parameters with their applications to a real data set.

Dans cette thèse nous étudions l'estimation de modèles probabilistes discriminants, surtout des aspects d'apprentissage semi-supervisé et de sélection de caractéristiques. Le but de l'apprentissage semi-supervisé est d'améliorer l'efficacité de l'apprentissage supervisé en utilisant des données non-étiquetées. Cet objectif est difficile à atteindre dans les cas des modèles discriminants. Les modèles probabilistes discriminants permettent de manipuler des représentations linguistiques riches, sous la forme de vecteurs de caractéristiques de très grande taille. Travailler en grande dimension pose des problèmes, en particulier computationnels, qui sont exacerbés dans le cadre de modèles de séquences tels que les champs aléatoires conditionnels (CRF). Notre contribution est double. Nous introduisons une méthode originale et simple pour intégrer des données non étiquetées dans une fonction objectif semi-supervisée. Nous démontrons alors que l'estimateur semi-supervisé correspondant est asymptotiquement optimal. Le cas de la régression logistique est illustré par des résultats d'expèriences. Dans cette étude, nous proposons un algorithme d'estimation pour les CRF qui réalise une sélection de modèle, par le truchement d'une pénalisation $L_1$. Nous présentons également les résultats d'expériences menées sur des tâches de traitement des langues (le chunking et la détection des entités nommées), en analysant les performances en généralisation et les caractéristiques sélectionnées. Nous proposons finalement diverses pistes pour améliorer l'efficacité computationelle de cette technique.

Every organization has to deal with planning of the appropriate level of human resources over time. The workforce is not always aligned with the requirements of the organization and it increases an organization’s budget. A literature review reveals that there is no model that can systematically predict accurate human resource required within a complex organization. To address this gap, a human resource predictive model was developed based on material requirements planning (MRP). This approach accounts for complexity in workforce planning and generalized it with a logistic regression model. The model estimates the employee turnover number and forecasts the expected remaining headcount for the next time period based on employee information such as; age, working year, salary, etc. Moreover, external variables and economic data can be utilized to adjust the estimated turnover probability. This model also suggests the possible internal workforce movement in case of in-house manpower imbalance.

Surface mining and subsequent reclamation often results in the establishment of large areas of grassland that can benefit wildlife. Grasslands have declined substantially over the last 150 years, resulting in declines of many grassland birds. The dickcissel (Spiza americana), a neotropical migrant, is one such bird whose numbers have declined in the last 30 years due to habitat loss, increased nest predation and parasitism, and over harvest (lethally controlled as an agricultural pest on its wintering range in Central and South America). Reclaimed surface-mined lands have been documented to provide important breeding habitat for dickcissels in the United States, emphasizing the importance of reclamation efforts. Objectives were to understand specific aspects of dickcissel nesting ecology (i.e., nest-site selection, nest success, and nest parasitism, and identification of nest predators) on 2 spatial scales on TXU Energy’s Big Brown Mine, near Fairfield, Texas, and to subsequently provide TXU Energy with recommendations to improve reclaimed areas as breeding habitat for dickcissels. I examined the influence of nest-site vegetation characteristics and the effects of field-level spatial factors on dickcissel nesting ecology on 2 sites reclaimed as wildlife habitat. Additionally, I developed a novel technique to identify predators at active nests during the 2003 field season. During 2002–2003, 119 nests were monitored. On smaller spatial scales, dickcissels were likely to select nest-sites with low vegetation, high densities of bunchgrasses and tall forbs, and areas with higher clover content. Probability of nest success increased with nest heights and vegetation heights above the nest, characteristics associated with woody nesting substrates. Woody nesting substrates were selected and bunchgrasses were avoided. Oak (Quercus spp.) saplings remained an important nesting substrate throughout the breeding season. On a larger scale, nest-site selection was likely to occur farther from wooded riparian areas and closer to recently-reclaimed areas. Nest parasitism was likely to occur near roads and wooded riparian areas. Results suggest reclaimed areas could be improved by planting more bunchgrasses, tall forbs (e.g., curly-cup gumweed [Grindelia squarrosa] and sunflower [Helianthus spp.]), clover (Trifolium spp.), and oaks (a preferred nesting substrate associated with higher survival rates). Larger-scale analysis suggests that larger tracts of wildlife areas should be created with wooded riparian areas comprising a minimal portion of a field’s edge.

This thesis presents a methodology for conducting logistic regression modeling of trip and household information obtained from household travel surveys and vehicle trip information obtained from global positioning systems (GPS) to better understand the trip underreporting that occurs. The methodology presented here builds on previous research by adding additional variables to the logistic regression model that might be significant in contributing to underreporting, specifically, trip purpose. Understanding the trip purpose is crucial in transportation planning because many of the transportation models used today are based on the number of trips in a given area by the purpose of a trip. The methodology used here was applied to two study areas in Texas, Laredo and Tyler-Longview. In these two study areas, household travel survey data and GPS-based vehicle tracking data was collected over a 24-hour period for 254 households and 388 vehicles. From these 254 households, a total of 2,795 trips were made, averaging 11.0 trips per household. By comparing the trips reported in the household travel survey with those recorded by the GPS unit, trips not reported in the household travel survey were identified. Logistic regression was shown to be effective in determining which household- and trip-related variables significantly contributed to the likelihood of a trip being reported. Although different variables were identified as significant in each of the models tested, one variable was found to be significant in all of them - trip purpose. It was also found that the household residence type and the use of household vehicles for commercial purposes did not significantly affect reporting rates in any of the models tested. The results shown here support the need for modeling trips by trip purpose, but also indicate that, from urban area to urban area, there are different factors contributing to the level of underreporting that occurs. An analysis of additional significant variables in each urban area found combinations that yielded trip reporting rates of 0%. Similar to the results of Zmud and Wolf (2003), trip duration and the number of vehicles available were also found to be significant in a full model encompassing both study areas.

Objectives: This Study wanted to analysis the characteristics of the elder adult who had hospitalized with pneumonia. We also evaluated the factors that will affect the unexpected readmission in elderly pneumonia patients. Methods: This is a retrospective cohort study design. The study data was collected 341 pneumonia patients who have hospitalized in a general teaching hospital in Kaohsiung city from year 2009 to 2010. The study population was divided into two groups, the sample size of the old group (age >= 65yrs), and the young group (age < 65yrs) was 173 and 168, respectively. The methods of stepwise multiple logistic regressions were needed to evaluate the association between aging and different days of unplanned readmission in adult pneumonia patients. Results: All the 341 adult pneumonia patients, we found 613 male and 926 female. The demography characteristic of the study subjects, the means of age was 61.9yrs (s.d. = 19.3yrs), and BMI was 23.4 kg/m2 (s.d. = 4.5 kg/m2). The percentage of ICD-9-CM that code 486 was 95.6%. Most patients were community-acquired pneumonia (98.8%), hospitalized from emergency room (85.3%), and admission in general wards (92.7%). The unplanned readmission within 14/30 days, 60 days, and 90days were 9.1%, 11.7%, and 15.0%, respectively. The significant factors that were associated with readmission within 14 days include age, Hb, hospitalized days, hypertension, and other disease. When we used the multiple logistic regression analysis to adjust the other variables, only age still significant with readmission within 14 days (the crude OR of the old group was 4.561, adjusted OR was 2.714, 95% CI of OR from 1.002 to 7.353). In the stepwise multiple logistic regression models, the variable that was associated with readmission with 14 or 30 days were age (>= 65yrs, OR = 3.025), WBC (>=10750 mm3, OR=2.917), and Hb (>=12.4 g/dL, OR=0.390). We remain the elderly subjects to evaluate the factor that will influence readmission states. In all the stepwise logistic regression models, we found the experience with used endotracheal tube in the hospitalized period were the significant increases the readmission rate within 14 or 30 days, 60 days, and 90 days. Conclusion: In our study shows that the situations of unexpected readmission in pneumonia patients were strong association with aging. We suggest that the indicator of medical quality should be adjusted before we comparison the readmission rate in the different institute. The major factors that will be associated to affect the readmission states were endotracheal tube used (significant with 14 or 30 days readmission rate), CRP level (significant with 60 days and 90 days readmission rate), and Hb level (significant with 60 days and 90 days readmission rate).

High quality defense personnel have decisive influence at modern war, and therefore it is the benefit for national security, and the root, garuantee for enhancing military combat power. For years, highly personnel resignation rate has been an important issue for militart personnel resources management. Abnormal resignation rate will not only influences the quality of organizational operation but also disr pts the experience of personnel of the organizational structure.Especially for military services,it will effect our national security and combat power as a whole. General studies of probing resignation were most focuset on factors of resignation will,tendency as probing issues,seldom studies were focused on systematic stuies of resignation rate. Therefore, it is a respond of human resources policies to probe resignation rate in an appropriate way. In this stay, the commissioned and non-commissioned offices in Chinese Marine Corp who stationed in southern Taiwan were taken as probing factors. The predictable capability of Logistic Regression Model has been used in this study as well in order to create the calculation model mode for resignanation rate. The result of the study has been comfirmed that educational level, part-time studies, seniority, marriage, ranking, branch of military services, salary, unit character, welfare and so on were all resignationrelared. Also it is acceptable to predict resignation rate by utilizing this method.

ABSTRACT This research aims at evaluating the risk of home loan, financial institutions generally think that makes enterprises to grant the loan and have a big risk in recent years , turning to and developing consumption financial transaction one after another, the personal home loan has already become the strategic point of every financial institution. The characteristic of the home loan is small for amount of money , there are many pieces , need to spend a large amount of manpower maintaining business operation , so it is fast and clear to verify the way , avoid lacking subjectively and reduce the risk , namely become one of the keys whether this business could be succeeded in promoting. So, the financial institution should set up a set of objective just awarding the way of evaluating the risk, is it verify personnel check and ratify loan amount fast, interest rate and award creed one while being other to help, reach the quantity, the goals of the standardization and automation. This research regards a domestic commercial bank as the main research object, and owe the parent in order to sample of case that is put of all loans now at present with this bank , 2431 normal samples , 381 bad samples of the above three months overdue , total 2812, and award the basic materials of the borrower of forms contained of letter application , and Joint Credit Information Center seek the credit materials that letter in the center inquires is the research range , analyse the background of different borrowers and overdue relation that make loans. Research this real example result can be summed up for as follows. The parameter of risk of showing of the loan is the sex, the age, academic credentials, grace period, family's annual income, round number of the borrowed money, the number of the cash card, interest rate, guarantee debts, whether it is the housing loan of a large number of types, collateral kind