Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Henry E. Fuelberg, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Sept. 24, 2004). Includes bibliographical references.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, 2005. / Title from title screen (viewed Jan. 31, 2006). "June 2005." Includes bibliographical references (p. 89-90). Also issued in paper format.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2005. / Thesis Advisor(s): Thomas W. Lucas, Richard Spainhour. Includes bibliographical references (p. 89-90). Also available online.

Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1743. Advisers: Wenyaw Chan; Glasser H. Jay. Includes bibliographical references.

The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix against not helix (H/∼H), sheet against not sheet (S/∼S) and coil against not coil (C/∼C). The performance of these binary classifiers are compared using the overall accuracy in predicting the protein secondary structure for various window sizes. Our result indicate that hold-out cross validation achieved higher accuracy than 5-fold cross validation. The Naive Bayes classifier, using 5-fold cross validation achieved, the lowest accuracy for predicting helix against not helix. The classification tree classifiers, using 5-fold cross validation, achieved the lowest accuracies for both coil against not coil and sheet against not sheet classifications. The logistic regression classier accuracy is dependent on the window size; there is a positive relationship between the accuracy and window size. The logistic regression classier approach achieved the highest accuracy when compared to the classification tree and Naive Bayes classifiers for each classification task; predicting helix against not helix with accuracy 77.74 percent, for sheet against not sheet with accuracy 81.22 percent and for coil against not coil with accuracy 73.39 percent. It is noted that it is easier to compare classifiers if the classification process could be completely facilitated in R. Alternatively, it would be easier to assess these logistic regression classifiers if SPSS had a function to determine the accuracy of the logistic regression classifier.

Meta-analysis refers to the quantitative synthesis of information across different studies. Since outcomes from different studies are likely to be reported in different units, study-level results are typically transformed to the same scale before quantitative integration. Typically, this leads to the accumulation and combination of effect sizes. To date, most social scientists have synthesized, or meta-analyzed, zero-order statistics like a correlation. Synthesizing partial effect sizes is an alternative which allows a meta-analysis to account for the influence of nuisance variables when estimating the association between two variables. This dissertation proposes that logistic regression coefficients from different studies, which are a type of partial effect size, can be meta-analyzed. Logistic regression models how a set of covariates relates to a binary dependent variable. Given a key independent variable (IV) of interest, which we can call the focal IV or Xf, the slope estimate (βf) in a logistic regression measures the impact of Xf on Y on the logit (log-odds) scale, while controlling for other variables. Four assumptions justify the possibility of comparing and possibly combing logistic slopes across studies: (1) Y must be on the same scale, (2) Xf must be on the same scale, (3) all effect sizes are logistic regression slopes adjusted for the same covariates, and (4) model specifications are identical. In practice, the third assumption is particularly challenging as different studies inevitably include different sets of control variables. Three simulation studies are implemented to understand how synthesizing a logistic regression slope on the logit scale is affected by several factors. Across these three simulation studies, the following meta-analytic variables are tested: (1) the size of the partial effect size (βf), (2) Study-level sample size (k), (3) Within-study sample size (N), (4) the degree of between-study variance, (5) a continuous vs. a binary focal predictor, (6) the level of collinearity between Xf and other covariates included in primary studies, (7) the magnitude of non-focal variable slopes, (8) different covariate sets used in primary-level studies, and (9) meta-analytical method. Simulation performance is based on how the bias and mean-squared error (MSE) are affected by each of these simulation parameters. Overall, results suggest that when the four assumptions introduced above are satisfied, meta-analyzing logistic regression slopes is remarkably accurate as the summary effect resulting from the standard random-effects meta-analytic model leads to small levels of bias and MSE under a variety of conditions. When the assumptions are broken (and particularly the third assumption of identical covariate sets), the pooled slope estimator can have large degrees of bias. The bias is a function of within-study sample size, between-study sample size, distribution of the focal IV (i.e., continuous vs. categorical variable), multicollinearity, the magnitude of non-focal variable slope parameters, diversity in covariate sets, and choice of meta-analytical methods. The MSE is a function of study-level sample size, within-study sample size, distribution of the focal IV (i.e., continuous vs. categorical variable), multicollinearity, the magnitude of non-focal variable slope parameters, diversity in covariate sets, and choice of meta-analytical methods. A complex four-way interaction is discovered between collinearity, the magnitude of non-focal variable slope parameters, diversity in covariate sets, and choice of meta-analytical methods. An applied example focusing on estimating the effects of albumin on mortality is also presented to complement the simulation results.

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This paper is exploring return migration in Sweden by implementing logistic regression technics on the cohort born 1978. In order to evaluate how socio-economic and geographical characteristics influence individuals propensities to re-circulate to the municipality of origin. Previous studies have indicated that socio-economic status is a selective trait that can either push or pull return migrants, depending on the setting. The theory of urban hierarchies was also applied to investigate if people were more likely to move back to certain region types. The calculations showed that 22 % of the movers had returned to their place of origin, with regional variations ranging from 18-30 %. The regression result revealed that a high socio-economic status decreased the likelihood of returning, while growing up in metropolitan city and having strong social capital in the place of origin increased the propensity. The findings were further supporting that movers have higher incomes than stayers, while return migrants gained less on their re-location in relation to all movers. I argue that these varying likelihoods depend on structural socio-economic divisions, which are pulling human capital to the metropolitan regions and causing a brain drain in the periphery. These population trends are replicating themselves over time and it is assumed that these processes are to enforce the regional disparities in the future.

The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the decision of which method to choose is primarily determined by variance pattern and prior probabilities. Under variance homogeneity, any of the methods may be recommended. However, LRA is recommended when priors are equal or extreme and linear PDA is recommended when priors are moderate. Under variance heterogeneity, selecting a recommended method is more complex. In many cases, more than one method could be used appropriately.

How does body armor and posture affect Soldier marksmanship? The Interceptor Body Armor (IBA) has significantly improved Soldier combat survivability, but in what ways does it change rifleman lethality? Moreover, can we model these effects so as to develop better tactics and operational plans? This study quantifies the effects of Soldier equipment on lethality through multi-factor logistic regression using data from range experiments with the 1st Brigade, 1st Infantry Division (Mechanized), at Fort Riley, Kansas. The designed experiment of this study estimates the probability of a qualified US rifleman hitting a human target. It uses the rifleman's equipment, posture, Military Occupational Specialty (MOS), and experience along with the target's distance, time exposure and silhouette presentation as input factors. The resulting family of mathematical models provides a Probability of Hit prediction tailored to a shooter-target scenario. The study shows that for targets closer than 150 meters, Soldiers shot better while wearing body armor than they did without. Body armor had a negative effect for targets farther than 200 meters, and this could significantly impact the employment of the Squad Designated Marksman. The study also shows that the kneeling posture is an effective technique and recommends standardized training on this method of firing.