Variable selection is an important topic in regression analysis and is intended to select the best subset of predictors. Least absolute shrinkage and selection operator (Lasso) was introduced by Tibshirani in 1996. This method can serve as a tool for variable selection because it shrinks some coefficients to exact zero by a constraint on the sum of absolute values of regression coefficients. For logistic regression, Lasso modifies the traditional parameter estimation method, maximum log likelihood, by adding the L1 norm of the parameters to the negative log likelihood function, so it turns a maximization problem into a minimization one. To solve this problem, we first need to give the value for the parameter of the L1 norm, called tuning parameter. Since the tuning parameter affects the coefficients estimation and variable selection, we want to find the optimal value for the tuning parameter to get the most accurate coefficient estimation and best subset of predictors in the L1 regularized regression model. There are two popular methods to select the optimal value of the tuning parameter that results in a best subset of predictors, Bayesian information criterion (BIC) and cross validation (CV). The objective of this paper is to evaluate and compare these two methods for selecting the optimal value of tuning parameter in terms of coefficients estimation accuracy and variable selection through simulation studies.

Drinking groundwater is the primary way humans accumulate arsenic. Chronic exposure to inorganic arsenic (iAs) (over decades) has been shown to be associated with multiple health effects at low levels (5-10 ppb) including: cancer, elevated blood pressure and cardiovascular disease, skin lesions, renal failure, and peripheral neuropathy. Using hypertension (or high blood pressure) as a surrogate marker for cardiovascular disease, we examined the effect of iAs alone and in a mixture with other metals using a cross-sectional study of adults in United States (National Health and Examination Survey, NHANES, 2005-2010) adjusting for covariates: urinary creatinine level (mg/dL), poverty index ratio (PIR, measure of socioeconomic status, 1 to 5), age, smoking (yes/no), alcohol usage, gender, non-Hispanic Black, and overweight (BMI>=25). A logistic regression model suggests that a one-unit increase in log of inorganic arsenic increases the odds of hypertension by a factor of 1.093 (95% Confidence Interval=0.935, 1.277) adjusted for these covariates , which indicates that there was not significant evidence to claim that inorganic arsenic is a risk factor for hypertension. Biomonitoring data provides evidence that humans are not only exposed to inorganic arsenic but also to mixtures of chemicals including inorganic arsenic, total mercury, cadmium, and lead. We tested for a mixture effect of these four environmental chemicals using weighted quantile sum (WQS) regression, which takes into account the correlation among the chemicals and with the outcome. For one-unit increase in the weighted sum, the adjusted odds of developing hypertension increases by a factor of 1.027 (95% CI=0.882,1.196), which is also not significant after taking into account the same covariates. The insignificant finding may be due to the low inorganic arsenic concentration (8-620 μg /L) in US drinking water, compared to those in countries like Bangladesh where the concentrations are much higher. Literature provides conflicting evidence of the association of inorganic arsenic and hypertension in low/moderate regions; future studies, especially a large cohort study, are needed to confirm if inorganic arsenic alone or with other metals is associated with hypertension in the United States.

The findings of this research indicate that volunteering is influenced by a number of factors, one of which is gender. The data used in this study reveal a different profile of the volunteer than is presented in much of the research on volunteering, which tends to profile the "most likely" volunteer as female, employed by the public sector, possessing a higher education and having children. The questions addressed in this research are: 1) What are the contextual effects of volunteering and 2) Is there a relationship of one or more of these effects to gender? The findings indicate men in this sample were not only more likely to volunteer, but were more likely to engage in volunteer activities that included political and civic roles. In addition, men were able to volunteer more hours as their family ties increased. The hours women volunteered were found to decrease as family ties increased. Women were less likely to volunteer for political and civic activities and more likely to volunteer for roles that included the care of children, elderly and family-oriented activities. These findings have implications for how volunteer activities contribute to the building of social and political resources for both men and women and bring to light how gendered definitions dominate patterns of civic engagement.

Purpose: Asthma can have significant adverse effects on the health and quality of life of children, and the prevalence of this condition continues to rise. Breastfeeding may protect against asthma, but some uncertainty remains. The purpose of this study was to further examine the relationship between breastfeeding and the risk of developing asthma in early childhood. Methods: Data were collected from the State and Local Area Integrated Telephone Survey: National Survey of Children's Health, 2003. The study population consisted of 33,315 children ages 0 to 5 years. Prevalence rates of asthma and breastfeeding ,were calculated, as were crude and Mantel-Haenszel summary odds ratios for breastfeeding and other potential confounders including age, race, education, poverty, and tobacco use. Logistic regression models were used to estimate odds ratios and 95% confidence intervals aRer adjustment for these confounders. Results: Breastfeeding (never vs. ever) was significantly associated with an increased odds ratio of asthma among the children surveyed (POR = 1.18, 95% CI = 1.04, 1.34). In addition, children with asthma had a slightly lower mean duration of breastfeeding than did children without asthma. However, a significant trend of increasing odds ratios with increasing duration of breastfeeding was not found. It therefore appears that the act of ever breastfeeding, regardless of duration, exerts some protective effect against the development of asthma in early childhood. Conclusions: Never breastfeeding was found to be significantly associated with the development of asthma in early childhood. Age, race, education, poverty level, and tobacco use were also implicated in this association. While further research is needed to fully determine the effectiveness of breastfeeding in the primary prevention of asthma, public health efforts should focus on promoting breastfeeding as it has the potential improve the overall health of children.

College completion is an issue of great concern in the United States, where only 50% of students who start college as freshmen complete a bachelor's degree at that institution within six years. Researchers have studied a variety of factors to understand their relationship to student persistence. Not surprisingly, student characteristics, particularly their academic background prior to entering college, have a tremendous influence on college success. Colleges and universities have little control over student characteristics unless they screen out lesser qualified students during the admissions process, but selectivity is contrary to the push for increased accessibility for under-served groups. As a result, institutions need to better understand the factors that they can control. High-impact educational practices have been shown to improve retention and persistence through increased student engagement. Service-learning, a pedagogical approach that blends meaningful community service and reflection with course content, is a practice that is increasing in popularity, and it has proven beneficial at increasing student learning and engagement. The purpose of this study was to investigate whether participation in service-learning has any influence in the likelihood of degree completion or time to degree and, secondarily, to compare different methods of analysis to determine whether use of more complex models provides better information or more accurate prediction. The population for this study was a large public urban research institution in the mid-Atlantic region, and the sample was the cohort of students who started as first-time, full-time, bachelor's degree-seeking undergraduates in the fall of 2005. Data included demographic and academic characteristics upon matriculation, as well as financial need and aid, academic major, and progress indicators for each of the first six years of enrollment. Cumulative data were analyzed using logistic regression, and year-to-year data were analyzed using discrete-time survival analysis in a structural equation modeling (SEM) framework. Parameter estimates and odds ratios for the predictors in each model were compared. Some similarities were found in the variables that predict degree completion, but there were also some striking differences. The strongest predictors for degree completion were pre-college academic characteristics and strength of academic progress while in college (credits earned and GPA). When analyzed using logistic regression and cross-sectional data, service-learning participation was not a significant predictor for completion, but it did have an effect on completion time for those students who earned a degree within six years. When analyzed longitudinally using discrete-time survival analysis, however, service-learning participation is strongly predictive of degree completion, particularly when credits are earned in the third, fourth, and sixth years of enrollment. In the survival analysis model, service-learning credits earned were also more significant for predicting degree completion than other credits earned. In terms of data analysis, logistic regression was effective at predicting completion, but survival analysis seems to provide a more robust method for studying specific variables that may vary by time.

The aim of the present work is to develop a tool for identification of Motor Third Party Liability insurance contracts which are at risk of cancellation. Methods for explorative data analysis, building a logistic regression model, comparing models and their validation and calibration are presented. Several models are developed on the real dataset using mentioned methods and then the final one is chosen. Behavior of the final model is verified by the validation on the out-of-time sample. Last step is calibration of the model to the expected value of the future portfolio cancellation rate.

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

Approved for public release, distribution is unlimited / This thesis examines the process of selecting the midshipmen leadership, or "stripers," at the United States Naval Academy. Using a qualitative approach, it gathers data from the current cohort of decision makers who select the stripers each semester regarding what they believe to be the desirable and undesirable qualities of striper candidates. Shifting to a quantitative approach, those qualities are then used to create variables using data from the Naval Academy classes of 1999 through 2002. A logistic regression is then estimated with the purpose of gauging if those qualities are, in fact, represented in the selectees. A model is presented which indicates that, by and large, the goals of the selection process are being met. Recommendations for minor policy adjustments and for further research are made based on the findings of both the qualitative and quantitative data. / http://hdl.handle.net/10945/985 / Lieutenant, United States Navy

The research conducted in this thesis examines the relationship between forms of worklessness (both active unemployment and inactivity due to sickness and disability) and mortality for working age men. Previous research has shown that being out of work is associated with a greater risk of mortality relative to being in work. However, there remains debate as to whether this association is the result of a causal pathway leading from worklessness to mortality or whether it reflects the ‘selection' of individuals who are already at greater risk of mortality from pre-existing poor health or other characteristics. In the UK, many studies rely on the use of ‘wear-off' periods in which mortality events occurring within five years after the observation of employment status are ignored to allow the confounding effects of selection to diminish. Generally these studies concluded in support of a causal relationship. In contrast, more recent studies making use of innovative methodological designs such as natural experiments and linked register and health datasets have found less evidence for this explanation with many emphasising the role of confounding and selection. The thesis aims to firstly, examine the effectiveness of wear-off periods and secondly, to develop an alternative counterfactual approach to examine the relationship between worklessness (both active unemployment and health related inactivity) and mortality. These questions are addressed in three stand-alone papers. In the first paper, data from the Scottish Longitudinal Study and the England & Wales Longitudinal Study was used in logistic regression models which estimated the odds of death in a given time period after the 1991 Census for those aged 35–64 in 1991. The odds ratios for the different economic positions (in work, unemployed, retired, permanently sick and other inactive) were compared, as well as the changes in risk associated with cumulatively increasing the length of wear-off prior to follow-up. No evidence was found of health related selection for the unemployed in 1991 suggesting that the use of the five year wear-off period in many studies of mortality and unemployment may be an ineffective and unnecessary technique for mitigating the effects of health-related selection. The second paper examined men aged between 35 and 54 who were in work in 1991. Subsequent employment status in 2001 was observed (in work or unemployed) and the relative all-cause mortality risk of unemployment between 2001 and 2007 was estimated. To account for potential selection into unemployment of those in poor health, a counterfactual propensity score matching framework was used to construct unbiased and comparable samples of in work and unemployed individuals. Matching was based on a wide range of explanatory variables including health status prior to year of unemployment (hospital admissions and self-reported limiting long term illness) as well as measures of socio-economic position. The findings showed that unemployment was associated with a doubling (hazard ratio 2.1 95% CI 1.30 - 3.38) of the subsequent risk of mortality from all causes relative to employment. This scale of effect was consistent across different samples and was robust controlling for prior health and socio-demographic characteristics. These findings were interpreted as evidence that the often observed association between unemployment and mortality may contain a causal component. The second paper implemented a similar analytical design to address the lack of evidence for the independent mortality effect of inactivity due to sickness. The results showed that the mortality risk of economic inactivity due to sickness relative to active employment was significant (HR. 3.18, 95% CI 2.53-3.98) and suggest that economic inactivity due to sickness poses a mortality risk that is independent of prior health. The findings could be interpreted in two ways; either economic inactivity due to sickness is worse for health than actively seeking work or previous studies of unemployment and mortality have underestimated the true effect of being out of work generally. Across the three studies, the main contribution of the thesis is to reassert the importance of worklessness as a determinant of individual mortality. In doing so the studies also found little evidence of systematic confounding by either health or other characteristics. The thesis concludes with a comprehensive discussion of the wider implications of the findings in relation to both general methodological issues in observational epidemiology and possible policy interventions that could be implemented to tackle work-related inequalities in male mortality.

"This project utilized generalized estimating equations and general linear modeling to model learning curves for sonographer performance in emergency ultrasonography. Performance was measured in two ways: image quality (interpretable vs. possible hindrance in interpretation) and agreement of findings between the sonographer and an expert reviewing sonographer. Records from 109 sonographers were split into two data sets-- training (n=50) and testing (n=59)--to conduct exploratory analysis and fit the final models for analysis, respectively. We determined that the number of scans of a particular exam type required for a sonographer to obtain quality images on that exam type with a predicted probability of 0.9 is highly dependent upon the person conducting the review, the indication of the scan (educational or medical), and the outcome of the scan (whether there is a pathology positive finding). Constructing family-wise 95% confidence intervals for each exam type demonstrated a large amount of variation for the number of scans required both between exam types and within exam types. It was determined that a sonographer's experience with a particular exam type is not a significant predictor of future agreement on that exam type and thus no estimates were made based on the agreement learning curves. In addition, we concluded based on a type III analysis that when already considering exam type related experience, the consideration of experience on other exam types does not significantly impact the learning curve for quality. However, the learning curve for agreement is significantly impacted by the additional consideration of experience on other exam types."