Purpose: Since September 11, 2001 military personnel have experienced a pattern of frequent deployment and reintegration, known as the deployment cycle. Deployments present unique challenges and opportunities to military personnel with lasting effects. This study examines group differences based on risk and protective factors, which were grouped into four domains (physical, mental, social, and spiritual) according to the Comprehensive Airman Fitness model in use by the U.S. Air Force to teach and increase resilience. The groups represent various levels of exposure to deployment dangers, up to and including combat, and time, recent deployment within two years and past deployment more than two years ago. Method: Secondary analysis was conducted with the 2013 Air Force Community Assessment Survey, a large, anonymous survey collected among U.S. Airmen. Discriminant analysis was utilized to determine and describe group differences. Results: The null hypothesis of no difference between group centroids was rejected. The primary group difference existed between Airmen who experienced combat and all other Airmen. The result of the discriminant analysis demonstrates at least two, possibly three, distinct groups exist among Airmen related to deployment experiences. The discriminant analysis generated six functions. Health and PTSD demonstrated the highest discriminant ability, although social support systems also played a significant role. Recent deployers reported higher levels of resilience and hardiness compared to past deployers regardless of exposure to deployment danger and combat. Meanwhile, past deployers reported higher levels of spirituality across all groups. Discussion: This study utilized aspects of resilience theory through the incorporation of time and a person-in-environment approach to the study of deployment and resilience. Implications related to social work practice include assessment of deployment frequency and the cumulative effects of deployment stressors. A specific policy recommendation is to ensure adequate leadership training in resilience promotion, as leadership represented an important component of resilience in this study. Finally, future research following this study could include qualitative analysis and studies utilizing more comprehensive scales among Airmen.

Ph.D. (Geography) / Please refer to full text to view abstract

The master's thesis aim is to construct an index of profitability of Czech banks using the method of discriminant analysis. The bases for construction of the index are commonly used ratios characteristic for the banking sector whose impact on profitability was evaluated as statistically significant. These indicators are calculated for a sample of 16 banks over three years; the individual observations were classified into one of two groups according to profitability. Besides the construction of the index and the subsequent description of the results, the work also deals with a brief description of the Czech banking sector and its development during the reference period. The theoretical part is mainly focused on description of the specifics of bank statements and interpretation of financial ratios used in the model. One chapter also discuss the basis of discriminant analysis.

Zhang, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 95-109). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Area of Machine Learning --- p.1 / Chapter 1.1.1 --- Types of Algorithms --- p.2 / Chapter 1.1.2 --- Modeling Assumptions --- p.4 / Chapter 1.2 --- Dimensionality Reduction --- p.4 / Chapter 1.3 --- Structure of the Thesis --- p.8 / Chapter 2 --- Dimensionality Reduction --- p.10 / Chapter 2.1 --- Feature Extraction --- p.11 / Chapter 2.1.1 --- Linear Feature Extraction --- p.11 / Chapter 2.1.2 --- Nonlinear Feature Extraction --- p.16 / Chapter 2.1.3 --- Sparse Feature Extraction --- p.19 / Chapter 2.1.4 --- Nonnegative Feature Extraction --- p.19 / Chapter 2.1.5 --- Incremental Feature Extraction --- p.20 / Chapter 2.2 --- Feature Selection --- p.20 / Chapter 2.2.1 --- Viewpoint of Feature Extraction --- p.21 / Chapter 2.2.2 --- Feature-Level Score --- p.22 / Chapter 2.2.3 --- Subset-Level Score --- p.22 / Chapter 3 --- Various Views of Feature Extraction --- p.24 / Chapter 3.1 --- Probabilistic Models --- p.25 / Chapter 3.2 --- Matrix Factorization --- p.26 / Chapter 3.3 --- Graph Embedding --- p.28 / Chapter 3.4 --- Manifold Learning --- p.28 / Chapter 3.5 --- Distance Metric Learning --- p.32 / Chapter 4 --- Tensor linear Laplacian discrimination --- p.34 / Chapter 4.1 --- Motivation --- p.35 / Chapter 4.2 --- Tensor Linear Laplacian Discrimination --- p.37 / Chapter 4.2.1 --- Preliminaries of Tensor Operations --- p.38 / Chapter 4.2.2 --- Discriminant Scatters --- p.38 / Chapter 4.2.3 --- Solving for Projection Matrices --- p.40 / Chapter 4.3 --- Definition of Weights --- p.44 / Chapter 4.3.1 --- Contextual Distance --- p.44 / Chapter 4.3.2 --- Tensor Coding Length --- p.45 / Chapter 4.4 --- Experimental Results --- p.47 / Chapter 4.4.1 --- Face Recognition --- p.48 / Chapter 4.4.2 --- Texture Classification --- p.50 / Chapter 4.4.3 --- Handwritten Digit Recognition --- p.52 / Chapter 4.5 --- Conclusions --- p.54 / Chapter 5 --- Semi-Supervised Semi-Riemannian Metric Map --- p.56 / Chapter 5.1 --- Introduction --- p.57 / Chapter 5.2 --- Semi-Riemannian Spaces --- p.60 / Chapter 5.3 --- Semi-Supervised Semi-Riemannian Metric Map --- p.61 / Chapter 5.3.1 --- The Discrepancy Criterion --- p.61 / Chapter 5.3.2 --- Semi-Riemannian Geometry Based Feature Extraction Framework --- p.63 / Chapter 5.3.3 --- Semi-Supervised Learning of Semi-Riemannian Metrics --- p.65 / Chapter 5.4 --- Discussion --- p.72 / Chapter 5.4.1 --- A General Framework for Semi-Supervised Dimensionality Reduction --- p.72 / Chapter 5.4.2 --- Comparison to SRDA --- p.74 / Chapter 5.4.3 --- Advantages over Semi-supervised Discriminant Analysis --- p.74 / Chapter 5.5 --- Experiments --- p.75 / Chapter 5.5.1 --- Experimental Setup --- p.76 / Chapter 5.5.2 --- Face Recognition --- p.76 / Chapter 5.5.3 --- Handwritten Digit Classification --- p.82 / Chapter 5.6 --- Conclusion --- p.84 / Chapter 6 --- Summary --- p.86 / Chapter A --- The Relationship between LDA and LLD --- p.89 / Chapter B --- Coding Length --- p.91 / Chapter C --- Connection between SRDA and ANMM --- p.92 / Chapter D --- From S3RMM to Graph-Based Approaches --- p.93 / Bibliography --- p.95

No description available.

A preliminary study of the feasibility of using categorical variables in discriminant function analysis was performed. Data including both continuous and categorical variables were used and predictive results examined. The discriminant function techniques were found to be robust enough to include the use of categorical variables. Some problems were encountered with using the trace criterion for selecting the most discriminating variables when these variables are categorical. No monotonic relationship was found to exist between the trace and the number of correct predictions. This study did show that the use of categorical variables does have much potential as a statistical tool in classification procedures. (50 pages)

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 118-123).

The aim of the current studies was to first determine the convergent validity of several observer and self-report measures of defense mechanisms, and second to determine whether participants in the depressed and anxious groups could successfully be differentiated using observer and self-report measures of defenses. In Study 1, defensive functioning of 150 university students was assessed using the Defense-Q, Defense Mechanism Rating Scale, Defense Style Questionnaire, and the Defense Mechanisms Inventory. The results of the Pearson r analyses indicated that the defense measures were correlated in a theoretically consistent manner at the overall and defense level analyses, with the strongest relations at the mature and immature ends of the scales. Four of the 17 individual defenses were correlated in a theoretically consistent manner. In Study 2, 1182 university students completed the Personality Assessment Inventory and those scoring in the clinical range on depression or anxiety indices were selected for participation in this study. The extent to which these participants could be correctly classified into their respective groups using defense scores from the Defense-Q and the Defense Style Questionnaire was assessed using discriminant analyses. Results indicated that defense scores from both observer and self-report measures can be used to classify participants correctly into depressed and anxious groups. The Defense-Q discriminant function primarily identified depression-related defenses as important for differentiation, whereas the Defense Style Questionnaire discriminant function primarily identified anxiety-related disorders. Confirmatory stepwise discriminant analyses confirmed that the defenses previously identified in the literature were among the most effective in differentiating between the groups. The results from the present investigation identify substantial differences between the defenses assessed by observer and self-report measures and indicate that both methods can be informative for differentiating between depressed and anxious participants.

Spectrum sensing is an essential pre-processing step of cognitive radio technology for dynamic radio spectrum management. One of the main functions of Cognitive radios is to detect the unused spectrum and share it without harmful interference with other users. The detection of signal components present within a determined frequency band is an important requirement of any sensing technique. Most methods are restricted to the detection of the spectral lines. However, these methods may not comply with the needs imposed by practical applications.  This master thesis work presents a novel method to detect significant spectral components in measured non-flat spectra by classifying them in two groups: signal and noise frequency lines. The algorithm based on Fisher’s discriminant analysis, aside from the detection of spectral lines, estimates the magnitude of the spectral lines and provides a measure of the quality of classification to determine if a spectral line was incorrectly classified. Furthermore, the frequency lines with higher probability of misclassification are regrouped and the validation process recomputed, which results in lower probabilities of misclassification. The proposed automatic detection algorithm requires no user interaction since any prior knowledge about the measured signal and the noise power is needed. The presence or absence of a signal regardless of the shape of the spectrum can be detected. Hence, this method becomes a strong basis for high-quality operation mode of cognitive radios. Simulation and measurement results prove the advantages of the presented technique. The performance of the technique is evaluated for different signal-to-noise ratios (SNR) ranging from 0 to -21dB as required by the IEEE standard for smart radios. The method is compared with previous signal detection methods.

The aim of the current studies was to first determine the convergent validity of several observer and self-report measures of defense mechanisms, and second to determine whether participants in the depressed and anxious groups could successfully be differentiated using observer and self-report measures of defenses. In Study 1, defensive functioning of 150 university students was assessed using the Defense-Q, Defense Mechanism Rating Scale, Defense Style Questionnaire, and the Defense Mechanisms Inventory. The results of the Pearson r analyses indicated that the defense measures were correlated in a theoretically consistent manner at the overall and defense level analyses, with the strongest relations at the mature and immature ends of the scales. Four of the 17 individual defenses were correlated in a theoretically consistent manner. In Study 2, 1182 university students completed the Personality Assessment Inventory and those scoring in the clinical range on depression or anxiety indices were selected for participation in this study. The extent to which these participants could be correctly classified into their respective groups using defense scores from the Defense-Q and the Defense Style Questionnaire was assessed using discriminant analyses. Results indicated that defense scores from both observer and self-report measures can be used to classify participants correctly into depressed and anxious groups. The Defense-Q discriminant function primarily identified depression-related defenses as important for differentiation, whereas the Defense Style Questionnaire discriminant function primarily identified anxiety-related disorders. Confirmatory stepwise discriminant analyses confirmed that the defenses previously identified in the literature were among the most effective in differentiating between the groups. The results from the present investigation identify substantial differences between the defenses assessed by observer and self-report measures and indicate that both methods can be informative for differentiating between depressed and anxious participants.