Zugl.: Köln, Univ., Diss., 2004

This paper aims to develop and validate an efficient method for delineation of public transit analysis zones (PTTAZ), particularly for origin-destination (OD) matrix prediction for transit operation planning. Existing methods have a problem in reflecting the level of spatial precision, travel characteristics, travel demand growth, access to transit stations, and most importantly, the direction of transit routes. This study proposes a new methodology to redelineate existing traffic analysis zones (TAZ) to create PTTAZ in order to allocate travel demand to transit stops. We aim to achieve an accurate prediction of the OD matrix for public transportation (PT). The matrix should reflect the passenger accessibility in the socioeconomic and socio-spatial characterization of PTTAZ and minimize intrazonal trips. The proposed methodology transforms TAZ-based to PTTAZ-based data with sequential steps through multiple statistical methods. In short, the generation of PTTAZ establishes homogeneous sub-zones representing the relationship between passenger flow, network structure, land use, population, socio-economic characteristics, and, most importantly, existing bus transit infrastructure. To validate the proposed scheme, we implement the framework for India’s Vishakhapatnam bus network and compare the results with the household survey. The results show that the PTTAZ-based OD matrix represents a realistic scenario for PT demand.

Missing values are a serious problem in surveys. The literature suggests to replace these with realistic values using imputation methods. This master thesis examines four different imputation techniques concerning their ability for handling missing data. Therefore, mean imputation, conditional mean imputation, Expectation-Maximization algorithm and Markov-Chain-Monte-Carlo method are presented. In addition, the three first mentioned methods were simulated by using a large real data set. To analyse the quality of these techniques a metric variable of the original data set was chosen to generate some missing values considering different percentages of missingness and common missing data mechanism. After the replacement of the simulated missing values, several statistical parameters, like quantiles, arithmetic mean and variance of all completed data sets were calculated in order to compare them with the parameters from the original data set. The results, that have been established by empiric data analysis, show that the Expectation-Maximization algorithm estimates all considered statistical parameters of the complete data set far better than the other analysed imputation methods, although the assumption of a multivariate normal distribution could not be achieved. It is found, that the mean as well as the conditional mean imputation produce statistically significant estimator for the arithmetic mean under the supposition of missing completely at random, whereas other parameters as the variance do not show the estimated effects. Generally, the accuracy of all estimators from the three imputation methods decreases with increasing percentage of missingness. The results lead to the conclusion that the Expectation-Maximization algorithm should be preferred over the mean and the conditional mean imputation.

This thesis describes two main projects; the first one is the optimization of a hierarchical search strategy to search for unknown pulsars. This project is divided into two parts; the first part (and the main part) is the semi-coherent hierarchical optimization strategy. The second part is a coherent hierarchical optimization strategy which can be used in a project like Einstein@Home. In both strategies we have found that the 3-stages search is the optimum strategy to search for unknown pulsars. For the second project we have developed a computer software for a coherent Multi-IFO (Interferometer Observatory) search. To validate our software, we have worked on simulated data as well as hardware injected signals of pulsars in the fourth LIGO science run (S4). While with the current sensitivity of our detectors we do not expect to detect any true Gravitational Wave signals in our data, we can still set upper limits on the strength of the gravitational waves signals. These upper limits, in fact, tell us how weak a signal strength we would detect. We have also used our software to set upper limits on the signal strength of known isolated pulsars using LIGO fifth science run (S5) data. / Diese Dissertation besteht aus zwei Projekten: Im ersten Projekt wird die Optimierung einer hierarchischen Strategie zum Auffinden von 'unbekannten' Pulsaren beschrieben. Der erste Teil besteht dabei aus einer semi-kohärenten und der zweite Teil aus einer kohärenten Optimierungsstrategie, wie sie in Projekten wie Einstein@Home verwendet werden kann. In beiden Ansätzen erwies sich eine 3-Stufensuche als optimale Suchstrategie für 'unbekannte' Pulsare. Für das zweite Projekt entwickelten wir eine Software für eine kohärente Multi-IFO (Interferometer Observatory) Suche. Zum Validieren der Software verwendeten wir sowohl simulierte Daten als auch Hardware induzierte Signale von Pulsaren aus dem vierten 'LIGO Science run' (S4). Wir erwarten nicht, mit der aktuellen Empfindlichkeit unserer Detektoren echte GW- Signale aufzunehmen, können jedoch obere Grenzen für die Stärke der Gravitationswellen-Signale bestimmen. Diese oberen Grenzen geben uns an, wie schwach ein gerade noch detektierbares Signal werden kann. Ferner benutzten wir die Software um eine obere Grenze für bekannte, isolierte Pulsare zu bestimmen, wobei wir Daten aus dem fünften 'LIGO Science run (S5) verwendeten.

Background: The PHQ-4 is a widely used open access screening instrument for depression and anxiety in different health care and community settings; however, empirical evidence of its psychometric quality in Colombia is lacking. The objectives of the current study were to generate normative data and to further investigate the construct validity and factorial structure of the PHQ-4 in the general population. Methods: A nationally representative face-to-face household survey was conducted in Colombia in 2012 (n = 1,500). The item characteristics of the PHQ-4 items, including the inter-item correlations and inter-subscale correlations, were investigated. To measure the scale’s reliability, the internal consistency (Cronbach’s α) was assessed. For factorial validity, the factor structure of the PHQ-4 was examined with confirmatory factor analysis (CFA). Results: The Cronbach’s alpha coefficient for the PHQ-4 was 0.84. The confirmatory factor analysis supported a two-factor model, which was structurally invariant between different age and gender groups. Normative data for the PHQ-4 were generated for both genders and different age levels. Women had significantly higher mean scores compared with men [1.4 (SD: 2.1) vs. 1.1 (SD: 1.9), respectively]. The results supported the discriminant validity of the PHQ-4. Conclusions: The normative data provide a framework for the interpretation and comparisons of the PHQ-4 with other populations in Colombia. The evidence supports the reliability and validity of the two-factor PHQ-4 as a measure of anxiety and depression in the general Colombian population.

Universiẗat, Diss., 2006--Marburg.

Universiẗat, Diss.--Köln, 2007.

Universiẗat, Diss.--Passau, 2007. / Literaturverz. S. 197 - 214.

Konstanz, Univ., Diplomarbeit, 2008.

Thesis (doctoral)--Göteborgs Universitet, 1993. / Errata sheet laid in. Includes bibliographical references.