This thesis consists of three papers in applied statistics, specifically in cluster sampling, causal inference, and measurement error. The first paper studies the problem of estimating the finite population mean from a two-stage sample with unequal selection probabilies in a Bayesian framework. Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. In a two-stage cluster sampling design, clusters are first selected with probability proportional to cluster size, and units are then randomly sampled within selected clusters. Methodological challenges arise when the sizes of nonsampled cluster are unknown. We propose both nonparametric and parametric Bayesian approaches for predicting the cluster size, and we implement inference for the unknown cluster sizes simultaneously with inference for survey outcome. We implement this method in Stan and use simulation studies to compare the performance of an integrated Bayesian approach to classical methods on their frequentist properties. We then apply our propsed method to the Fragile Families and Child Wellbeing study as an illustration of complex survey inference. The second paper focuses on the problem of weak instrumental variables, motivated by estimating the causal effect of incarceration on recidivism. An instrument is weak when it is only weakly predictive of the treatment of interest. Given the well-known pitfalls of weak instrumental variables, we propose a method for strengthening a weak instrument. We use a matching strategy that pairs observations to be close on observed covariates but far on the instrument. This strategy strengthens the instrument, but with the tradeoff of reduced sample size. To help guide the applied researcher in selecting a match, we propose simulating the power of a sensitivity analysis and design sensitivity and using graphical methods to examine the results. We also demonstrate the use of recently developed methods for identifying effect modification, which is an interaction between a pretreatment covariate and the treatment. Larger and less variable treatment effects are less sensitive to unobserved bias, so identifying when effect modification is present and which covariates may be the source is important. We undertake our study in the context of studying the causal effect of incarceration on recividism via a natural experiment in the state of Pennsylvania, a motivating example that illustrates each component of our analysis. The third paper considers the issue of measurement error in the context of survey sampling and hierarchical models. Researchers are often interested in studying the relationship between community-levels variables and individual outcomes. This approach often requires estimating the neighborhood-level variable of interest from the sampled households, which induces measurement error in the neighborhood-level covariate since not all households are sampled. Other times, neighborhood-level variables are not observed directly, and only a noisy proxy is available. In both cases, the observed variables may contain measurement error. Measurement error is known to attenuate the coefficient of the mismeasured variable, but it can also affect other coefficients in the model, and ignoring measurement error can lead to misleading inference. We propose a Bayesian hierarchical model that integrates an explicit model for the measurement error process along with a model for the outcome of interest for both sampling-induced measurement error and classical measurement error. Advances in Bayesian computation, specifically the development of the Stan probabilistic programming language, make the implementation of such models easy and straightforward.

Purpose – The financial systems in many emerging countries are still impacted by the devastating effect of the 2008 financial crisis which created a massive disaster in the global economy. The banking sector needs appropriate quantitative techniques to assess its financial soundness, strengths and weaknesses. This research aims to explore, empirically assess and analyze the financial soundness of the banking sector in Kazakhstan. It also examines the prediction of financial unsoundness at an individual bank level using PCA, cluster, MDA, logit and probit analyses. Design/Methodology/Approach – A cluster analysis, in combination with principal component analysis (PCA), was utilized as a classification technique. It groups sound and unsound banks in Kazakhstan's banking sector by examining various financial ratios. Cluster analysis was run on a sample of 34 commercial banks on 1st January, 2008 and 37 commercial banks on 1st January, 2014 to test the ability of this technique to detect unsound banks before they fail. Then, Altman Z” and EM Score models were tested and re-estimated and the MDA, logit and probit models were constructed on a sample of 12 Kazakhstan banks during the period between 1st January, 2008 and 1st January, 2014. The sample consists of 6 sound and 6 unsound banks and accounts for 81.3% of the total assets of the Kazakhstan banking sector in 2014. These statistical methods used various financial variables to represent capital adequacy, asset quality, management, earnings and liquidity. Last but not least, the MDA, logit and probit models were systematically combined together to construct an integrated model to predict bank financial unsoundness. Findings – First of all, results from Chapter 3 indicate that cluster analysis is able to identify the structure of the Kazakh banking sector by the degree of financial soundness. Secondly, based on the findings in the second empirical chapter, the tested and re-estimated Altman models show a modest ability to predict bank financial unsoundness in Kazakhstan. Thirdly, the MDA, logit and probit models show high predictive accuracy in excess of 80%. Finally, the model that integrated the MDA, logit and probit types presents superior predictability with lower Type I errors. Practical Implications – The results of this research are of interest to supervisory and regulatory bodies. The models can be used as a reliable and effective tool, particularly the cluster based methodology for assessing the degree of financial soundness in the banking sector and the integrated model for predicting the financial unsoundness of banks. Originality/Value – This study is the first to employ a cluster-based methodology to assess financial soundness in the Kazakh banking sector. In addition, the integrated model can be used as a promising technique for evaluating the financial unsoundness of banks in terms of predictive accuracy and robustness. Importance – Assessing the financial soundness of the Kazakh banking system is of particular importance as the World Bank has ranked Kazakhstan as leading the world for the volume of non-performing credits in the total number of loans granted in 2012. It is one of the first academic studies carried out on Kazakhstan banks which comprehensively evaluate the financial soundness of banks. It is anticipated that the findings of the current study will provide useful lessons for developing and transition countries during periods of financial turmoil.

by Yiu Ming Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 92-99). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Bayesian YING-YANG Learning Theory and Number Selec- tion Criterion --- p.5 / Chapter 1.2 --- General Motivation --- p.6 / Chapter 1.3 --- Contributions of the Thesis --- p.6 / Chapter 1.4 --- Other Related Contributions --- p.7 / Chapter 1.4.1 --- A Fast Number Detection Approach --- p.7 / Chapter 1.4.2 --- Application of RPCL to Prediction Models for Time Series Forecasting --- p.7 / Chapter 1.4.3 --- Publications --- p.8 / Chapter 1.5 --- Outline of the Thesis --- p.8 / Chapter 2 --- Open Problem: How Many Clusters? --- p.11 / Chapter 3 --- Bayesian YING-YANG Learning Theory: Review and Experiments --- p.17 / Chapter 3.1 --- Briefly Review of Bayesian YING-YANG Learning Theory --- p.18 / Chapter 3.2 --- Number Selection Criterion --- p.20 / Chapter 3.3 --- Experiments --- p.23 / Chapter 3.3.1 --- Experimental Purposes and Data Sets --- p.23 / Chapter 3.3.2 --- Experimental Results --- p.23 / Chapter 4 --- Conditions of Number Selection Criterion --- p.39 / Chapter 4.1 --- Alternative Condition of Number Selection Criterion --- p.40 / Chapter 4.2 --- Conditions of Special Hard-cut Criterion --- p.45 / Chapter 4.2.1 --- Criterion Conditions in Two-Gaussian Case --- p.45 / Chapter 4.2.2 --- Criterion Conditions in k*-Gaussian Case --- p.59 / Chapter 4.3 --- Experimental Results --- p.60 / Chapter 4.3.1 --- Purpose and Data Sets --- p.60 / Chapter 4.3.2 --- Experimental Results --- p.63 / Chapter 4.4 --- Discussion --- p.63 / Chapter 5 --- Application of Number Selection Criterion to Data Classification --- p.80 / Chapter 5.1 --- Unsupervised Classification --- p.80 / Chapter 5.1.1 --- Experiments --- p.81 / Chapter 5.2 --- Supervised Classification --- p.82 / Chapter 5.2.1 --- RBF Network --- p.85 / Chapter 5.2.2 --- Experiments --- p.86 / Chapter 6 --- Conclusion and Future Work --- p.89 / Chapter 6.1 --- Conclusion --- p.89 / Chapter 6.2 --- Future Work --- p.90 / Bibliography --- p.92 / Chapter A --- A Number Detection Approach for Equal-and-Isotropic Variance Clusters --- p.100 / Chapter A.1 --- Number Detection Approach --- p.100 / Chapter A.2 --- Demonstration Experiments --- p.102 / Chapter A.3 --- Remarks --- p.105 / Chapter B --- RBF Network with RPCL Approach --- p.106 / Chapter B.l --- Introduction --- p.106 / Chapter B.2 --- Normalized RBF net and Extended Normalized RBF Net --- p.108 / Chapter B.3 --- Demonstration --- p.110 / Chapter B.4 --- Remarks --- p.113 / Chapter C --- Adaptive RPCL-CLP Model for Financial Forecasting --- p.114 / Chapter C.1 --- Introduction --- p.114 / Chapter C.2 --- Extraction of Input Patterns and Outputs --- p.115 / Chapter C.3 --- RPCL-CLP Model --- p.116 / Chapter C.3.1 --- RPCL-CLP Architecture --- p.116 / Chapter C.3.2 --- Training Stage of RPCL-CLP --- p.117 / Chapter C.3.3 --- Prediction Stage of RPCL-CLP --- p.122 / Chapter C.4 --- Adaptive RPCL-CLP Model --- p.122 / Chapter C.4.1 --- Data Pre-and-Post Processing --- p.122 / Chapter C.4.2 --- Architecture and Implementation --- p.122 / Chapter C.5 --- Computer Experiments --- p.125 / Chapter C.5.1 --- Data Sets and Experimental Purpose --- p.125 / Chapter C.5.2 --- Experimental Results --- p.126 / Chapter C.6 --- Conclusion --- p.134 / Chapter D --- Publication List --- p.135 / Chapter D.1 --- Publication List --- p.135

Li, Qiuhui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 46-47). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- An overview of the BitTorrent protocol --- p.3 / Chapter 2 --- Problem Formulation --- p.7 / Chapter 2.1 --- Type-based Peer Selection Algorithm --- p.7 / Chapter 2.2 --- Clustering Index --- p.9 / Chapter 3 --- Model Formulation --- p.11 / Chapter 3.1 --- Markovian Model --- p.12 / Chapter 3.2 --- Transition Matrix --- p.14 / Chapter 3.2.1 --- Search Process --- p.16 / Chapter 3.2.2 --- Match Process --- p.18 / Chapter 3.2.3 --- Cut Process --- p.19 / Chapter 3.3 --- Open System Model --- p.21 / Chapter 4 --- Numerical Results and Observations --- p.24 / Chapter 4.1 --- Clustering Index --- p.24 / Chapter 4.2 --- Upload Utilization --- p.26 / Chapter 4.3 --- Download Rate --- p.28 / Chapter 4.4 --- Open System --- p.30 / Chapter 5 --- Performance Evaluation --- p.32 / Chapter 5.1 --- Model Verification --- p.34 / Chapter 5.2 --- Control the Clustering Index --- p.36 / Chapter 6 --- Related Works --- p.40 / Chapter 7 --- Conclusions --- p.44 / Bibliography --- p.46

Fung, Chun Yip. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 53-54). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- The Exchange Algorithm Framework --- p.4 / Chapter 2.1 --- Ordinary Least Squares Linear Regression --- p.5 / Chapter 2.2 --- The Exchange Algorithm --- p.6 / Chapter 3 --- Methodology --- p.12 / Chapter 3.1 --- Least Median Squares Linear Regression --- p.12 / Chapter 3.2 --- Least Median Squares Algorithm for Clusterwise Linear Re- gression --- p.16 / Chapter 3.3 --- Measures of Performance --- p.20 / Chapter 3.4 --- An Illustrative Example --- p.24 / Chapter 4 --- Monte Carlo Simulation Study --- p.34 / Chapter 4.1 --- Simulation Plan --- p.34 / Chapter 4.2 --- Simulation Results --- p.41 / Chapter 4.2.1 --- Effects of the Six factors --- p.41 / Chapter 4.2.2 --- Comparisons between LMSA and the Exchange Algorithm --- p.47 / Chapter 4.2.3 --- Evaluation of the Improvement of Regression Parame- ters by Performing Stage 3 in LMSA --- p.50 / Chapter 5 --- Concluding Remarks --- p.51 / Bibliography --- p.52

Licensed valuers in the State of Victoria, Australia currently appraise rural land using manual techniques. Manual techniques typically involve site visits to the property, liaison with property owners through interview, and require a valuer experienced in agricultural properties to determine a value. The use of manual techniques typically takes longer to determine a property value than for valuations performed using automated techniques, providing appropriate data are available. Manual methods of valuation can be subjective and lead to bias in valuation estimates, especially where valuers have varying levels of experience within a specific regional area. Automation may lend itself to more accurate valuation estimates by providing greater consistency between valuations. Automated techniques presently in use for valuation include artificial neural networks, expert systems, case based reasoning and multiple regression analysis. The latter technique appears mo st widely used for valuation. The research aimed to develop a conceptual rural property valuation model, and to develop and evaluate quantitative models for rural property valuation based on the variables identified in the conceptual model. The conceptual model was developed by examining peer research, Valuation Best Practice Standards, a standard in use throughout Victoria for rating valuations, and rural property valuation texts. Using data that are only available digitally and publicly, the research assessed this conceptualisation using properties from four LGAs in the Wellington and Wimmera Catchment Management Authority (CMAs) areas in Victoria. Cluster analysis was undertaken to assess if the use of sub-markets, that are determined statistically, can lead to models that are more accurate than sub-markets that have been determined using geographically defined areas. The research is divided into two phases; the 'available data phase' and the 'restricted data phase'. The 'available data phase' used publicly available digital data to build quantitative models to estimate the value of rural properties. The 'restricted data phase' used data that became available near the completion of the research. The research examined the effect of using statistically derived sub-markets as opposed to geographically derived ones for property valuation. Cluster analysis was used during both phases of model development and showed that one of the clusters developed in the available data phase was superior in its model prediction compared to the models produced using geographically derived regions. A number of limitations with the digital property data available for Victoria were found. Although GIS analysis can enable more property characteristics to be derived and measured from existing data, it is reliant on having access to suitable digital data. The research also identified limitations with the metadata elements in use in Victoria (ANZMETA DTD version 1). It is hypothesised that to further refine the models and achieve greater levels of price estimation, additional properties would need to be sourced and added to the current property database. It is suggested that additional research needs to address issues associated with sub-market identification. If results of additional modelling indicated significantly different levels of price estimation, then these models could be used with manual techniques to evaluate manually derived valuation estimates.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Under the Clean Water Act (CWA) program the Texas Commission on Environmental Quality (TCEQ) listed 110 stream segments with pathogenic bacteria impairment in 2000. The current study was conducted to characterize the watersheds associated with the impaired waterbodies. The main characteristics considered for the classification of waterbodies were designated use of the waterbody, land use distribution, density of stream network, average distance of a land of a particular use to the closest stream, household population, density of on-site sewage facilities (OSSF), bacterial loading due to the presence of different types of farm animals and wildlife, and average climatic conditions. The availability of observed in-stream fecal coliform bacteria concentration data was evaluated to obtain subgroups of data-rich and data-poor watersheds within a group. The climatic data and observed in-stream fecal coliform bacteria concentrations were analyzed to find out seasonal variability of the water quality. The watershed characteristics were analyzed using the multivariate statistical analysis techniques such as factor analysis/principal component analysis, cluster analysis, and discriminant analysis. Six groups of watersheds were formed as result of the statistical analysis. The main factors that differentiate the clusters were found to be bacterial contribution from farm animals and wildlife, density of OSSF, density of households connected to public sewers, and the land use distribution. Two watersheds were selected each from two groups of watersheds. Hydrological Simulation Program-FORTRAN (HSPF) model was calibrated for one watershed within each group and tested for the other watershed in the same group to study the similarity in the parameter sets due to the similarity in watershed characteristics. The study showed that the watersheds within a given cluster formed during the multivariate statistical analysis showed similar watershed characteristics and yielded similar model results for similar model input parameters. The effect of parameter uncertainty on the in-stream bacterial concentration predictions by HSPF was evaluated for the watershed of Salado Creek, in Bexar County. The parameters that control the HSPF model hydrology contributed the most variance in the in-stream fecal coliform bacterial concentrations corresponding to a simulation period between 1 January 1995 and 31 December 2000.

Much is known about childbirth fear in Sweden including its relationship to caesarean birth. Less is understood about this in Australia. Sweden has half the rate of caesarean birth compared to Australia. Little has been reported about women’s beliefs and attitudes to birth in either country. The contribution of psychosocial factors such as fear, attitudes and beliefs about childbirth to the global escalation of caesarean birth in high-income countries is an important topic of debate. The overall aim of this thesis is to investigate the prevalence and impact of fear on birthing outcomes in two cohorts of pregnant women from Australia and Sweden and to explore the birth attitudes and beliefs of these women.   A prospective longitudinal cohort study from two towns in Australia and Sweden (N=509) was undertaken in the years 2007-2009. Pregnant women completed self-report questionnaires at mid-pregnancy, late pregnancy and two months after birth. Fear of birth was measured in mid-pregnancy with a tool developed in this study: the Fear of Birth Scale (FOBS). The FOBS showed promise as a clinically practical way to identify women with significant fear. A similar prevalence of fear of birth (30 percent) was found in the Australian and Swedish cohorts (Paper I).  The Swedish women had attitudes indicating a greater concern for the personal impacts of birth and a belief system that situated birth as a natural event when compared to the Australian women (Paper II). Finally, when women’s attitudes and levels of fear were combined, three profiles were identified: Self determiners, Take it as it comes and Fearful (Paper III). Belonging to the Fearful profile had the most negative outcomes for women including higher rates of elective caesarean, more negative feelings in pregnancy and post birth and poorer perceptions of the quality of their antenatal and intra-partum care (Paper IV).

In the aftermath of the events of 9/11, the U.S. executive branch has repeatedly maintained that its need for action to secure the nation requires a revised interpretation of individual liberties. This study will explore the tensions between the positive ideographs and in response to the negative ideograph in a contemporary United States court ruling. Using Burke’s pentad, and cluster analysis, as well as Brummett’s notion of strategic silence, the study examines how the FISCR substantially changed the interrelationship between the two ideographs. The study concludes that the FISCR situated strengthening national security as the purpose of the case it ruled on, which privileged national security over privacy. Throughout the expansion of security,> the court used silence to justify its decision. This analysis both adds to our understanding of the synchronic relationship between ideographs, and examines how the courts utilize such interplays to reconstitute community.