No description available.

Thesis (Ph.D.)--Baylor University, 2007. / Includes bibliographical references (p. 96-98).

Thesis (M.S. in Operations Research)--Naval Postgraduate School, Sept. 2004. / Thesis Advisor(s): Samuel E. Buttrey. Includes bibliographical references (p. 73-74). Also available online.

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

The problem considered relates to estimating an arbitrary regression function m(x) from sample pairs (Xᵢ,Yᵢ) 1 ≤ i ≤ n. A model is assumed of the form Y = m(x) + ε(x) where ε(x) is a random variable with expectation 0. One well known method for estimating m(x) is by using one of a class of kernel regression estimators say m(n)(x). Schuster (1972) has shown conditions under which the limiting distribution of the kernel estimator m(n)(x) is the normal distribution. It might also be of interest to use the data to estimate the distribution of m(n)(x). One could, given this estimate, construct approximate confidence bounds for the function m(x). Three estimators are proposed for the density of m(n)(x). They share a basis in non-parametric kernel regression and utilize bootstrap techniques to obtain the density estimate. The order of convergence of one of the estimators is examined and conditions are given under which the order is higher then when estimation is by the normal approximation. Finally the performance of each estimator for constructing confidence bounds is compared for moderate sample sizes using computer studies.

No description available.

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Approved for public release; distribution is unlimited. / This thesis analyzes the impact of different educational credentials on first-term attrition by enlisted sailors in the U.S. Navy. For enlistment screening, the Navy currently categorizes applicants in one of three tiers according to educational attainment. These tiers form the basis of the Recruit Quality Matrix, which employs Armed Forces Qualification Test scores and educational credentials to determine enlistment eligibility. The analysis draws primarily from two sources: a Defense Manpower Data Center file containing enlisted cohorts from fiscal years 1989 through 1997 (to assess first-term attrition), and a Commander, Navy Recruiting Command data base containing enlisted cohorts from fiscal years 1998 through 2003 (to examine bootcamp attrition). Logit regression models are constructed using these data to identify differences in attrition propensities within the general tiers. A refined matrix is designed and evaluated as a more accurate predictor of attrition. Further research is recommended to look at additional measures of success in service, such as performance, productivity, and promotion. / Lieutenant Commander, United States Navy

This is an empirical study using a logistic regression model to assess the impact of mobilization and unemployment on an individual's decision to stay in or leave the reserves. The goal is to find out the attrition behavior of USMCR participants in order to better establish recruiting and retention goals in the Reserve population. Questions regarding attrition influencers, effects of mobilization, and applicability to both officer and enlisted personnel were reviewed in this process. The effects of being called to active service are shown to have a positive effect on retention in the reserves. Similarly, serving in the SMCR and Stand-by Reserves are both shown in the model to have a positive effect on reserve retention. This makes sense, in that when an individual volunteers in the Marine Reserves, he or she evidences a desire to serve his country when called to do so. The negative effect of an increase in the number of days served on active duty, as shown in the results of the model, follows similar logic. Had the individual wanted to serve on a full-time active duty basis he would have volunteered for the active duty component. The longer he is asked to remain on active duty, the more dissatisfied he is, on average, with his participation in the reserves. The negative effect of an increase in the individual's home of record unemployment rate is also consistent with previous findings, and when combined with the negative effect of continued mobilization and recall from the IRR or a retired status, a significant negative impact is seen on the individual's decision to stay in. The findings indicate that multiple short activations have a positive impact, whereas the impact of fewer, lengthy activations is negative This study validated previous research regarding the likelihood to continue to serve in the Marine Corps Reserves. As a result, the Marine Corps has the potential to better allocate resources and schedule individual activations, reducing attrition. This can assist in shaping the force structure when the Marine Corps are needed.