Background: Many cancer patients who require external beam radiotherapy such as breast cancer patients, present with irregular surface topographies and tissue inhomogenieties in the treatment field. Such irregularities give rise to unacceptable dose non-uniformity. Standard fields cannot be applied without compensation for missing tissue. 1-D and 2-D missing tissue compensators can be used but they have limitations. 3-D compensators are the most effective but they are normally fabricated using very expensive automated systems. Objectives: To study the variation of linear attenuation coefficients of different materials in megavoltage photon beams, select a tissue equivalent compensating material and commission a local 3-D manual missing tissue compensator cutter. Methods and materials: Linear attenuation coefficients were measured for tin, River sand mix, Lincolnshire bolus and dental modelling wax for different energy megavoltage photon beams. Measurements were done in a water phantom using a cylindrical ionisation chamber at varying depths. The CT numbers and densities of the materials were also measured. Negative plaster of paris moulds of the breast and head and neck areas were made using a RANDOTM Alderson anthropomorphic phantom from typically simulated fields. 3-D missing tissue compensators were then fabricated on the manual cutter and were tested for their effectiveness during treatment delivery. Results: Linear attenuation coefficients were dependent on photon beam energy, the thickness and density of the attenuator, but independent of the depth of measurement for compensator thickness of more than 2 cm. Lincolnshire bolus and dental modelling wax with CT numbers of –78 ± 9 and -88 ± 18 and densities of 1.4 ± 0.0 g/cm3 and 0.9 ± 0.0 g/cm3 respectively can be regarded as tissue equivalent materials. The fabricated 3-D missing tissue compensators were effective in correcting for dose non-uniformities compared to fields with no beam-modifying devices or wedges (1-D compensators). Conclusions: The 3-D missing tissue compensators were effective in correcting for dose non-uniformities in treatment fields involving very irregular surface topographies compared to 1-D and 2-D methods. They can be fabricated cheaply using a 3-D manual missing tissue compensator cutter. Quality control procedures need to be followed during fabrication.

by Chiu Yiu Ming. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 127-129.

by Man-lai Tang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1991. / Includes bibliographical references. / Chapter PART I ： --- ANALYSIS OF DATA WITH POLYTOMOUS VARIABLES --- p.1 / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Estimation of the Model with Incomplete Data --- p.5 / Chapter §2.1 --- The Model --- p.5 / Chapter §2.2 --- Two-stage Estimation Method --- p.7 / Chapter Chapter 3 --- Generalization to Several Populations --- p.16 / Chapter §3.1 --- The Model --- p.16 / Chapter §3.2 --- Two-stage Estimation Method --- p.18 / Chapter Chapter 4 --- Computation of the Estimates --- p.23 / Chapter §4.1 --- Maximum Likelihood Estimates in Stage I --- p.23 / Chapter §4.2 --- Generalized Least Squares Estimates in Stage II --- p.27 / Chapter §4.3 --- Approximation for the weight matrix W --- p.28 / Chapter Chapter 5 --- Some Illustrative Examples --- p.31 / Chapter §5.1 --- Single Population --- p.31 / Chapter §5.2 --- Multisample --- p.37 / Chapter PART II ： --- ANALYSIS OF CONTINUOUS AND POLYTOMOUS VARIABLES --- p.42 / Chapter Chapter 6 --- Introduction --- p.42 / Chapter Chapter 7 --- Several Populations Structural Equation Models with Continuous and Polytomous Variables --- p.44 / Chapter §7.1 --- The Model --- p.44 / Chapter §7.2 --- Analysis of the Model --- p.45 / Chapter Chapter 8 --- Analysis of Structural Equation Models of Polytomous and Continuous Variables with Incomplete Data by Multisample Technique --- p.54 / Chapter §8.1 --- Motivation --- p.54 / Chapter §8.2 --- The Model --- p.55 / Chapter §8.3 --- The Method --- p.56 / Chapter Chapter 9 --- Computation of the Estimates --- p.60 / Chapter §9.1 --- Optimization Procedure --- p.60 / Chapter §9.2 --- Derivatives --- p.61 / Chapter Chapter 10 --- Some Illustrative Examples --- p.65 / Chapter §10.1 --- Multisample Example --- p.65 / Chapter §10.2 --- Incomplete Data Example --- p.67 / Chapter §10.3 --- The LISREL Program --- p.69 / Chapter Chapter 11 --- Conclusion --- p.71 / Tables --- p.73 / Appendix --- p.85 / References --- p.89

by Kwan-Moon Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 77-79. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Estimation of the Model with Some Polytomous Entries Missed --- p.5 / Chapter §2.1 --- The Model --- p.5 / Chapter §2.2 --- Full Maximum Likelihood (FML) Estimation --- p.7 / Chapter Chapter 3 --- Estimation of the Model with Some Continuous and Polytomous Entries Missed --- p.13 / Chapter §3.1 --- The Model --- p.13 / Chapter §3.2 --- Pseudo Maximum Likelihood (PsML) Estimation --- p.15 / Chapter Chapter 4 --- Indirect Methods --- p.19 / Chapter §4.1 --- Listwise Deletion Method --- p.19 / Chapter §4.2 --- Mean Imputation Method --- p.19 / Chapter §4.3 --- Regression Imputation Method --- p.20 / Chapter Chapter 5 --- Computation of the Estimates --- p.23 / Chapter §5.1 --- Optimization Procedure --- p.23 / Chapter §5.2 --- Starting Value and Gradient Vector of the Model with Some Polytomous Entries Missed --- p.25 / Chapter §5.3 --- Starting Value and Gradient Vector of the Model with Some Continuous and Polytomous Entries Missed --- p.29 / Chapter Chapter 6 --- Partition Maximum Likelihood (PML) Estimation --- p.35 / Chapter §6.1 --- Motivation --- p.35 / Chapter §6.2 --- PML Procedure of the Model with Some Polytomous Entries Missed --- p.35 / Chapter §6.3 --- PML Procedure of the Model with Some Continuous and Polytomous Entries Missed --- p.37 / Chapter Chapter 7 --- Simulation Studies and Comparison --- p.39 / Chapter §7.1 --- Simulation Study I --- p.39 / Chapter §7.2 --- Simulation Study II --- p.44 / Chapter Chapter 8 --- Summary and Discussion --- p.43 / Tables / Appendix / References

The semi-parametric approach to the analysis of proportional hazards survival data is relatively new, having been initiated in 1972 by Sir David Cox, who restricted its use to hypothesis tests and confidence intervals for fixed effects in a regression setting. Practitioners have begun to diversify applications of this model, constructing residuals, modeling the baseline hazard, estimating median failure time, and analyzing experiments with random effects and repeated measures. The main purpose of this thesis is to show that working with an incompletely specified loglikelihood is more fruitful than working with Cox's original partial loglikelihood, in these applications. In Chapter 2, we show that the deviance residuals arising naturally from the partial loglikelihood have difficulties detecting outliers. We demonstrate that a smoothed, nonparametric baseline hazard partially solves this problem. In Chapter 3, we derive new deviance residuals that are useful for identifying the shape of the baseline hazard. When these new residuals are plotted in temporal order, patterns in the residuals mirror patterns in the baseline hazard. In Chapter 4, we demonstrate how to analyze survival data having a split-plot design structure. Using a BLUP estimation algorithm, we produce hypothesis tests for fixed effects, and estimation procedures for the fixed effects and random effects. / Graduation date: 1999

People who go missing are often perceived to have done so voluntarily, and yet, many missing persons in Canada are Aboriginal, visible minorities, homeless, and are fleeing from violence, abuse, and neglect. Integrating the concept of social exclusion and an intersectional perspective with a sample of 724 missing persons cases drawn from one Canadian police service, this dissertation examines the systemic issues underlying peoples’ disappearances. This dissertation also explores the role of social and economic disadvantage in the risk of a long term disappearance. A combination of univariate (descriptions), bivariate (cross-tabulations), and multivariate (logistic regression) analyses identify correlates and causes of going missing and correlates and causes of long term disappearances. The concept of social exclusion explains how structural processes prevent particular groups and individuals from gaining access to valued social relationships and economic opportunities in a particular society, resulting in considerable hardship and disadvantage. This dissertation argues that people who are marginalized and excluded have few resources to rely on to cope with stress and strain and may resort to going missing if confronted with adversity. Groups who are overrepresented among missing persons compared to the general population are identified by cross-tabulations and chi-square tests. Multivariate analysis (partial tables and logistic regression) is used to control for possible sources of spuriousness, in order to have more confidence in imputing causal relationships between membership in disadvantaged groups and going missing. Moreover, if disadvantaged groups go missing, they further sever ties with families, the labour market, and other mainstream institutions. As a result of extreme disadvantage, they may find it difficult to (re)connect with conventional social relationships and mainstream society. For example, youth who are escaping violence and abuse at home often end up on the streets and sever ties with schools, families, and other conventional support networks and become engaged in street culture. As a result of extreme disadvantage these young people are at risk of a long term disappearance. In other words, social exclusion is expected to be a risk and causal factor in long term disappearances. Groups who are overrepresented among long term disappearances compared to short term disappearances are identified by cross-tabulations and chi-square tests. Logistic regression analysis is used to draw conclusions about causal factors in long term disappearances. This research finds that excluded groups such as disadvantaged youth, Aboriginal people, women and other visible minorities, victims of violence, and youth in care are at disproportionate risk of going missing. Consistent with an intersectional perspective, this dissertation shows that certain groups who are multiply marginalized such as Aboriginal women and young women face an especially high risk of going missing. Aboriginal identity, labour force status, and homelessness are also implicated as causal factors in peoples’ disappearances. Moreover, this research finds that social exclusion is a risk and causal factor in long term disappearances as Aboriginal people, homeless people, minorities and other excluded groups face a high risk of a long term disappearance. Linking missing persons with the concept of social exclusion highlights the role of structural issues in peoples’ disappearances and refutes the common misperception that going missing is a choice. In terms of policy, the findings from this research indicate that prevention and intervention depend on targeting poverty, discrimination, gender inequality, violence, and other structural issues associated with social exclusion.

Hierarchical models are applicable to modeling data from complex surveys or longitudinal data when a clustered or multistage sample design is employed. The focus of this thesis is to investigate inference for discrete hierarchical models in the presence of missing data. This thesis is divided into two parts: in the first part, methods are developed to analyze the discrete and ordinal response data from hierarchical longitudinal studies. Several approximation methods have been developed to estimate the parameters for the fixed and random effects in the context of generalized linear models. The thesis focuses on two likelihood-based estimation procedures, the pseudo likelihood (PL) method and the adaptive Gaussian quadrature (AGQ) method. The simulation results suggest that AGQ is preferable to PL when the goal is to estimate the variance of the random intercept in a complex hierarchical model. AGQ provides smaller biases for the estimate of the variance of the random intercept. Furthermore, it permits greater flexibility in accommodating user-defined likelihood functions. In the second part, simulated data are used to develop a method for modeling longitudinal binary data when non-response depends on unobserved responses. This simulation study modeled three-level discrete hierarchical data with 30% and 40% missing data using a missing not at random (MNAR) missing-data mechanism. It focused on a monotone missing data-pattern. The imputation methods used in this thesis are: complete case analysis (CCA), last observation carried forward (LOCF), available case missing value (ACMVPM) restriction, complete case missing value (CCMVPM) restriction, neighboring case missing value (NCMVPM) restriction, selection model with predictive mean matching method (SMPM), and Bayesian pattern mixture model. All three restriction methods and the selection model used the predictive mean matching method to impute missing data. Multiple imputation is used to impute the missing values. These m imputed values for each missing data produce m complete datasets. Each dataset is analyzed and the parameters are estimated. The results from the m analyses are then combined using the method of Rubin(1987), and inferences are made from these results. Our results suggest that restriction methods provide results that are superior to those of other methods. The selection model provides smaller biases than the LOCF methods but as the proportion of missing data increases the selection model is not better than LOCF. Among the three restriction methods the ACMVPM method performs best. The proposed method provides an alternative to standard selection and pattern-mixture modeling frameworks when data are not missing at random. This method is applied to data from the third Waterloo Smoking Project, a seven-year smoking prevention study having substantial non-response due to loss-to-follow-up.

As the advances of communication techniques, some services related to location information came into existence successively. On such application is on finding out the mobile groups that exhibit spatial and temporal proximities called mobile group mining. Although there exists positioning devices that are capable of achieving a high accuracy with low measurement error. Many consumer-grades, inexpensive positioning devices that incurred various extent of higher measurement error are much more popular. In addition, some natural factors such as temperature, humidity, and pressure may have influences on the precision of position measurement. Worse, moving objects may sometimes become untraceable voluntarily or involuntarily. In this thesis, we extend the previous work on mobile group mining and adopt Kalman filter to correct the noisy data and predict the missing data. Several methods based on Kalman filter that correct/predict either correction data or pair-wise distance data. These methods have been evaluated using synthetic data generated using IBM City Simulator. We identify the operating regions in which each method has the best performance.

In recent years, sustainable development has become one of the most important issues internationally. Many indicators related to sustainable development have been proposed and implemented, such as Island Taiwan and Urban Taiwan. However the missing values come along with environmental monitoring data pose serious problems when we conducted the study on building a sustainable development indicator for marine environment. Since data is the origin of the summarized information, such as indicators. Given the poor data quality caused by the missing values, there will be some doubts about the result accuracy when using such data set for estimation. It is therefore important to apply suitable data pre-processing, such that reliable information can be acquired by advanced data analysis. Several reasons cause the problem of missing value in environmental monitoring data, for example: breakdown of machines, ruin of samples, forgot recording, mismatch of records when merging data, and lost of records when processing data. The situations of missing data are also diverse, for example: in the same time of sampling, some data records at several sampling sites are partially or completely disappeared. On the contrary, partial or complete time series data are missing at the same sampling site. It is therefore obvious to see that the missing values of environmental monitoring data are both related to spatial and temporal dimensions. Currently the techniques of data imputation have been developed for certain types of data or the interpolation of missing values based on either geographic data distributions or time-series functions. To accommodate both spatial and temporal information in an analysis is rarely seen. The current study has been tried to integrate the related analysis procedures and develop a computing process using both spatial and temporal dimensions inherent in the environmental monitoring data. Such data imputation process can enhance the accuracy of estimated missing values.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.