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.

Contemporary computer vision solutions to the problem of object detection aim at incorporating contextual information into the process. This thesis proposes a systematic evaluation of the usefulness of incorporating knowledge about the geometric context of a scene into a baseline object detection algorithm based on local features. This research extends publicly available MATLABRª implementations of leading algorithms in the field and integrates them in a coherent and extensible way. Experiments are presented to compare the performance and accuracy between baseline and context-based detectors, using images from the recently published SUN09 dataset. Experimental results demonstrate that adding contextual information about the geometry of the scene improves the detector performance over the baseline case in 50% of the tested cases. / by Rafael J. Giusti Urbina. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Project (M.S.)--Brigham Young University. Dept. of Statistics, 2006. / Includes bibliographical references (p. 66-67).

The main goal of this study is to seek the effects of some risk and prognostic factors contributing to survival of female invasive breast cancer in United States. The study presents the survival analysis for the adult female invasive breast cancer based on the datasets chosen from the Surveillance Epidemiology and End Results (SEER) program of National Cancer Institute (NCI). In this study, the Cox proportional hazard regression model and logistic regression model were employed for statistical analysis. The odds ratios (OR), hazard ratios (HR) and confidence interval (C.I.) were obtained for the risk and prognosis factors. The study results showed that some risk and prognosis factors, such as the demographic factors (race and age), social and family factor (marital status), biomedical factors (tumor size, disease stage, tumor markers and tumor cell differentiation level etc.) and type of treatment patients received had significant effects on survival of the female invasive breast cancer patients.

The paper constructs recidivism prediction models for women drug prisoners, using the 10 factors evaluated in "drug recidivism risk assessment form" by correctional institutions and the 18 factors studied in the literature. With the new recidivism prediction model, I hope to help improving the prediction accuracy of women drug prisoners¡¦ recidivism. The sample in the paper includes 1,029 drug prisoners released from Kaohsiung Women's Prison between 2008 and 2011. All criminal records are traced until the end of 2011. Two sets of potential risk factors of recidivism are considered in the paper. The first set only contains the factors in the evaluation form, and the second set includes all relevant factors. Using Logistic Regression Analysis and Survival Analysis, the effects of potential risk factors on recidivism are examined. I also predict the probability and the time interval of recidivism. Using the Logistic regression model with the risk factors only in the evaluation form, 58.4% of recidivism can be correctly predicted. While extending the set of potential risk factors, the screening rate of recidivism can be enhanced to 73.3%. The median forecast results are far superior to the average forecast in Survival Analysis. With the potential risk factors in the evaluation form, the difference of predicted recidivism date and the actual date is less than 60 days and less than 180 days in 2.5% and 9.6% of sample respectively. With all relevant risk factors, prediction, the share of sample whose difference of predicted recidivism date and the actual date is less than 60 days and less than 180 days are significantly improved to 10.2% and 27.3% respectively.

Thesis (Ph. D.)--Texas State University-San Marcos, 2007. / Vita. Includes bibliographical references (leaves 111-129).

Thesis (Ph.D.)--Baylor University, 2008. / Includes bibliographical references (p. 69-71)

Thesis (M.S.)--Duquesne University, 2004. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 26).

Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008. / Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.

Thesis (M.S.)--Brigham Young University. Dept. of Audiology and Speech-Language Pathology, 2006. / Includes bibliographical references (p. 38-43).