The prediction of a company's financial health is of critical importance to a variety of stakeholders ranging from auditors, creditors, customers, employees, financial institutions and investors through to management. There has been considerable research in this field, ranging from the univariate dichotomous approach of Beaver (1966) to the multivariate multi-state approaches of Lau (1987) and Ward (1994). All of the South African studies namely, Strebel and Andrews (1977), Daya (1977), De La Rey (1981), Clarke et al (1991) and Court et al (1999), and even, Lukhwareni's (2005) four separate models, were dichotomous in nature providing either a "Healthy" or a "Failed" state; or a "Winner" or "Loser" as in the latter case. Notwithstanding, all of these models would be classified as first stage, initial screening models. This study has focused on following a two stage approach to identifying (first stage) and analysing (second stage) the States of Health in a company. It has not adopted the rigid "Healthy" or "Failed" dichotomous methodology. For the first stage, three-state models were developed classifying a company as Healthy, Intermittent or Distressed. Both three year and five year Profit after Tax (PAT) averages for Real Earnings Growth (REG) calculations were used to determine the superior definition for the Intermittent state; with the latter coming out as superior. Models were developed for the current year (Yn), one (Yn-1), two (Yn-2) and three years (Yn-3) forward using a Test sample of twenty companies and their predictive accuracy determined by using a Holdout sample of twenty-two companies and all their data points or years of information. The statistical methods employed were a Naïve model using the simple Shareholder Value Added (SVA) ratio, CHAID and MDA, with the latter providing very disappointing results - for the Yn year (five year average), the Test sample results were 100%, 95% and 95%, respectively; with the Holdout sample results being 81.3%, 83.8% and 52.5%, respectively. The Yn-1 to Yn-3 models produced very good results for the Test sample but somewhat disappointing Holdout sample results. The best two Yn models namely, the Naïve and the CHAID models, were modified so as to enable a comparison with the notable, dichotomous De La Rey (1981) model. As such, three different approaches were adopted and in all cases, both the modified Naïve (100%, 81.3%, 100%) and the modified CHAID (100%, 85.9%, 98%) produced superior results to the De La Rey model (84.8%, 62.6%, 75.3%). For the second stage, a Financial Risk Analysis Model (FRAM) using ratios in the categories of Growth, Performance Analysis, Investment Analysis and Financial Status were used to provide underlying information or clues, independent of the first stage model, so as to enable the stakeholder to establish a more meaningful picture of the company. This would pave the way for the appropriate strategy and course of action to be followed, to take the company to the next level; whether it be taking the company out of a Distressed State (D) or further improving on its Healthy status (H). / Business Management / D. BL.

Saskatchewan is the only province in Canada to have routinely tested the antimicrobial susceptibility of all provincially reported human cases of campylobacteriosis. From 1999 to 2006, 1378 human Campylobacter species infections were tested for susceptibility at the Saskatchewan Disease Control Laboratory using the Canadian Integrated Program for Antimicrobial Resistance Surveillance panel and minimum inhibitory concentration (MIC) breakpoints. Of these, 1200 were C. jejuni, 129 were C. coli, with the remaining made up of C. lari, C. laridis, C. upsaliensis and undifferentiated Campylobacter species. Campylobacter coli had significantly higher prevalences of ciprofloxacin resistance (CIPr), erythromycin resistance (ERYr), combined CIPr-ERYr resistance and multidrug resistance (to three or greater drug classes) than C. jejuni. Logistic regression models indicated that CIPr in C. jejuni decreased from 1999 to 2004 and subsequently increased in 2005 and 2006. The risk of CIPr was significantly increased in the winter months (January to March) compared to other seasons. A comparison of logistic regression and Cox proportional hazard survival models found that the latter were better able to detect significant temporal trends in CIPr and tetracycline resistance by directly modeling MICs, but that these trends were more difficult to interpret. Scan statistics detected significant spatial clusters of CIPr C. jejuni infections in urban centers (Saskatoon and Regina) and temporal clusters in the winter months; the space-time permutation model did not detect any space-time clusters. Bernoulli scan tests were computationally the fastest for cluster detection, compared to ordinal MIC and multinomial antibiogram models. eBURST analysis of antibiogram patterns showed a marked distinction between case and non-case isolates from the scan statistic clusters. Multilevel logistic regression models detected significant individual and regional contextual risk factors for infection with CIPr C. jejuni. Patients infected in the winter, that were between the ages of 40-45 years of age, that lived in urban regions and that lived in regions of moderately high poultry density had higher risks of a resistant infection. These results advance the epidemiologic knowledge of CIPr C. jejuni in Saskatchewan and provide novel analytical methods for antimicrobial resistance surveillance data in Canada. / Saskatchewan Disease Control Laboratory (Saskatchewan Ministry of Health); Laboratory for Foodborne Zoonoses (Public Health Agency of Canada); Centre for Foodborne, Environmental and Zoonotic Infectious Diseases (Public Health Agency of Canada); Ontario Veterinary College Blake Graham Fellowship

The prediction of a company's financial health is of critical importance to a variety of stakeholders ranging from auditors, creditors, customers, employees, financial institutions and investors through to management. There has been considerable research in this field, ranging from the univariate dichotomous approach of Beaver (1966) to the multivariate multi-state approaches of Lau (1987) and Ward (1994). All of the South African studies namely, Strebel and Andrews (1977), Daya (1977), De La Rey (1981), Clarke et al (1991) and Court et al (1999), and even, Lukhwareni's (2005) four separate models, were dichotomous in nature providing either a "Healthy" or a "Failed" state; or a "Winner" or "Loser" as in the latter case. Notwithstanding, all of these models would be classified as first stage, initial screening models. This study has focused on following a two stage approach to identifying (first stage) and analysing (second stage) the States of Health in a company. It has not adopted the rigid "Healthy" or "Failed" dichotomous methodology. For the first stage, three-state models were developed classifying a company as Healthy, Intermittent or Distressed. Both three year and five year Profit after Tax (PAT) averages for Real Earnings Growth (REG) calculations were used to determine the superior definition for the Intermittent state; with the latter coming out as superior. Models were developed for the current year (Yn), one (Yn-1), two (Yn-2) and three years (Yn-3) forward using a Test sample of twenty companies and their predictive accuracy determined by using a Holdout sample of twenty-two companies and all their data points or years of information. The statistical methods employed were a Naïve model using the simple Shareholder Value Added (SVA) ratio, CHAID and MDA, with the latter providing very disappointing results - for the Yn year (five year average), the Test sample results were 100%, 95% and 95%, respectively; with the Holdout sample results being 81.3%, 83.8% and 52.5%, respectively. The Yn-1 to Yn-3 models produced very good results for the Test sample but somewhat disappointing Holdout sample results. The best two Yn models namely, the Naïve and the CHAID models, were modified so as to enable a comparison with the notable, dichotomous De La Rey (1981) model. As such, three different approaches were adopted and in all cases, both the modified Naïve (100%, 81.3%, 100%) and the modified CHAID (100%, 85.9%, 98%) produced superior results to the De La Rey model (84.8%, 62.6%, 75.3%). For the second stage, a Financial Risk Analysis Model (FRAM) using ratios in the categories of Growth, Performance Analysis, Investment Analysis and Financial Status were used to provide underlying information or clues, independent of the first stage model, so as to enable the stakeholder to establish a more meaningful picture of the company. This would pave the way for the appropriate strategy and course of action to be followed, to take the company to the next level; whether it be taking the company out of a Distressed State (D) or further improving on its Healthy status (H). / Business Management / D. BL.

In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year 3 and year 6. Instead of analysing each process separately, joint modelling is proposed for the trivariate outcomes to account for the inherent association by introducing random effects and the covariance matrix G. The influence of covariance matrix G on statistical inference of fixed-effects parameters has been investigated within the Bayesian framework. The study shows that by joint modelling the multivariate longitudinal process, it can reduce the bias and provide with more reliable results than it does by modelling each process separately. Together with the longitudinal measurements taken intermittently, a counting process of events in time is often being observed as well during a longitudinal study. It is of interest to investigate the relationship between time to event and longitudinal process, on the other hand, measurements taken for the longitudinal process may be potentially truncated by the terminated events, such as death. Thus, it may be crucial to jointly model the survival and longitudinal data. It is popular to propose linear mixed-effects models for the longitudinal process of continuous outcomes and Cox regression model for survival data to characterize the relationship between time to event and longitudinal process, and some standard assumptions have been made. In chapter 3, we try to investigate the influence on statistical inference for survival data when the assumption of mutual independence on random error of linear mixed-effects models of longitudinal process has been violated. And the study is conducted by utilising conditional score estimation approach, which provides with robust estimators and shares computational advantage. Generalised sufficient statistic of random effects is proposed to account for the correlation remaining among the random error, which is characterized by the data-driven method of modified Cholesky decomposition. The simulation study shows that, by doing so, it can provide with nearly unbiased estimation and efficient statistical inference as well. In chapter 4, it is trying to account for both the current and past information of longitudinal process into the survival models of joint modelling. In the last 15 to 20 years, it has been popular or even standard to assume that longitudinal process affects the counting process of events in time only through the current value, which, however, is not necessary to be true all the time, as recognised by the investigators in more recent studies. An integral over the trajectory of longitudinal process, along with a weighted curve, is proposed to account for both the current and past information to improve inference and reduce the under estimation of effects of longitudinal process on the risk hazards. A plausible approach of statistical inference for the proposed models has been proposed in the chapter, along with real data analysis and simulation study.

La littérature sur la diversification criminelle s’est particulièrement développée au cours des deux dernières décennies. Parmi les études, la diversité dans les activités illicites a été examinée auprès de divers sous-échantillons. Toutefois, aucune n’a été menée sur la versatilité criminelle de fournisseurs de drogues. Pourtant, ces derniers se distinguent quant à plusieurs aspects de leurs activités criminelles. L’objectif principal de ce mémoire est de déterminer quels sont les facteurs ayant une influence sur la diversification criminelle des distributeurs de drogues au Québec. Pour ce faire, un sous-échantillon de 33 270 fournisseurs de drogues a été circonscrit à l’aide de données d’arrestations policières des années 2003 à 2009. Le résultat émergeant d’une régression logistique ordinale est que les facteurs de diversification criminelle sont essentiellement les mêmes pour notre sous-échantillon et les autres délinquants. Nous constatons également que nous gagnons en rigueur en distinguant les fournisseurs spécialisés dans un marché illicite, des distributeurs dits polydrogues. En effet, les analyses indiquent que ces derniers s’impliquent dans un nombre restreint de types de crimes. Finalement, contrairement à ce qui a été observé dans la littérature sur la diversité criminelle, certaines variables n’ont que peu ou pas d’effet sur la versatilité dans les activités illicites, comme l’intensité criminelle ainsi que l’âge. / The literature on criminal diversification was developed particularly over the past two decades. Among the studies, diversity in illicit activities has been examined in various subsamples. However, no study has been conducted on the criminal versatility of drug providers. However, they differ in several aspects of their criminal activities. The main objective of this work is to determine the factors that influence the criminal diversification of drug distributors in Quebec. To do this, we use a sub-sample of 33,270 drug providers, created using police arrest data from the years 2003 to 2009. The main result of an ordinal logistic regression is that the factors of criminal diversification are essentially the same for our subsample and other offenders. We also note that we are gaining in rigour by distinguishing suppliers specialized in an illicit market from the so-called polydrugs distributors. Indeed, analyses indicate that these polydrugs distributors are involved in a limited number of crime types. Finally, contrary to what has been observed in the literature on criminal diversity, some variables have little or no effect on versatility in illicit activities, such as criminal intensity and age.

Text in English / An increasingly recognised prevention approach for substance use entails reduction in risk factors and enhancement of promotive or protective factors in individuals and the environment surrounding them during their growth and development. However, in order to enhance the effectiveness of this approach, continuous study of risk aspects targeting different cultures, social groups and mixture of society has been recommended. This study evaluated the impact of potential risk and protective factors associated with family management and relations on adolescent substance abuse in South Africa. Exploratory analysis and cumulative odds ordinal logistic regression modelling was performed on the data while controlling for demographic and socio-economic characteristics on adolescent substance use. The most intensely used substances were tobacco, cannabis, cocaine, heroin and alcohol in decreasing order of use intensity. The specific protective or risk impact of family management or relations factors varied from substance to substance. Risk factors associated with demographic and socio-economic factors included being male, younger age, being in lower education grades, coloured ethnicity, adolescents from divorced parents and unemployed or fully employed mothers. Significant family relations risk and protective factors against substance use were classified as either family functioning and conflict or family bonding and support. Several family management factors, categorised as parental monitoring, discipline, behavioural control and rewards, demonstrated either risk or protective effect on adolescent substance use. Some factors had either interactive risk or protective impact on substance use or lost significance when analysed jointly with other factors such as controlled variables. Interaction amongst risk or protective factors as well as the type of substance should be considered when further considering interventions based on these risk or protective factors. Studies in other geographical regions, institutions and with better gender balance are recommended to improve upon the representativeness of the results. Several other considerations to be made when formulating interventions, the shortcomings of this study and possible improvements as well as future studies are also suggested. / Psychology / M. A. (Psychology)

本文主要是在探討三個變數所構成之三維列聯表中，兩兩有序類別變數間的關係，而衡量的標準，我們則採用「兩兩變數所構成之二維列聯表中，相鄰兩列與相鄰兩行所求計出的相對成敗比（local odds ratios）」。在三維列聯表的資料架構下，我們可分別就固定某一變數水準之下兩個有序變數彼此間的「條件關係」，以及三個有序類別變數彼此兩兩間的「部分關係」，建構其各自的三維關係模型，並進行參數估計。此外，我們也提供必要的電腦程式，並舉出實例，加以說明。 / In analyzing a three-way contingency table with three ordinal variables, we can use association models suggested in Goodman (1979) to study the association between each pair of ordinal variables. The association was measured in terms of the local odds ratios formed from adjacent rows and adjacent columns of the cross-classification. This article investigates in great details the conditional association models and the partial association models for three-way cross-classifications. In addition, issues on estimating the para-meters in these two kinds of association models are discussed, and computer programs are provided. Some of the applications are illustrated.

Text in English / An increasingly recognised prevention approach for substance use entails reduction in risk factors and enhancement of promotive or protective factors in individuals and the environment surrounding them during their growth and development. However, in order to enhance the effectiveness of this approach, continuous study of risk aspects targeting different cultures, social groups and mixture of society has been recommended. This study evaluated the impact of potential risk and protective factors associated with family management and relations on adolescent substance abuse in South Africa. Exploratory analysis and cumulative odds ordinal logistic regression modelling was performed on the data while controlling for demographic and socio-economic characteristics on adolescent substance use. The most intensely used substances were tobacco, cannabis, cocaine, heroin and alcohol in decreasing order of use intensity. The specific protective or risk impact of family management or relations factors varied from substance to substance. Risk factors associated with demographic and socio-economic factors included being male, younger age, being in lower education grades, coloured ethnicity, adolescents from divorced parents and unemployed or fully employed mothers. Significant family relations risk and protective factors against substance use were classified as either family functioning and conflict or family bonding and support. Several family management factors, categorised as parental monitoring, discipline, behavioural control and rewards, demonstrated either risk or protective effect on adolescent substance use. Some factors had either interactive risk or protective impact on substance use or lost significance when analysed jointly with other factors such as controlled variables. Interaction amongst risk or protective factors as well as the type of substance should be considered when further considering interventions based on these risk or protective factors. Studies in other geographical regions, institutions and with better gender balance are recommended to improve upon the representativeness of the results. Several other considerations to be made when formulating interventions, the shortcomings of this study and possible improvements as well as future studies are also suggested. / Psychology / M. A. (Psychology)