Tourism is acquiring the attention worldwide especially in the developing countries (Akpabio et al. 2006). In the case of coastal areas, tourism is more sensitive to environmental degradation compared to other economic activities because the environment is its primary resource. With the help of efficient planning and proper management of natural resources, tourism can significantly contribute to environmental conservation and to achieve environmental sustainability in coastal areas (Orhon et al. 2011). The main purposes of this study are to explore the tourism development trend in coastal areas of Cox’s Bazar, to analyse existing environmental conditions of Cox’s Bazar coastal area, to examine the importance of environmental sustainability at coastal region. More positively, the study shows environmental sustainability can be achieved through more eco-friendly planning of installations in tourism hubs like Cox’s Bazar. The main methods of conducting this study were desktop research; data collection through questionnaire survey and expert interview; data input in SPSS, processing and analysis; evaluation of policies, strategies and institutional framework. To formulate study goal and objectives, a comprehensive literature review has been conducted to understand about tourism, tourism development, sustainability, sustainable development by reviewing relevant reports, journals, and international cases which has helped to develop the conceptual framework of the study. After extensive literature review and formulation of goal and objectives, the conceptual framework of the study data collection instruments such as questionnaire has been prepared to collect data from the field. The study is largely based on the primary data collected through field visit, interviews to the experts on tourism and environment, and questionnaire survey at the study area, Cox’s Bazar. In this study, the statistical data on the study area has been collected from Bangladesh Bureau of Statistics (BBS). Apart from this, information has also been collected from various sources e.g. journals, projects, periodicals, and the daily newspapers, archives of both home and abroad. From the analysis, it found that Cox’s Bazar sea beach is a good place for tourism development, and it is also found that the level of tourism is improving gradually. Tourism in Cox’s Bazar mainly depends on natural beauty and environment of the coastal area. Tourism is producing long term negative effects on the coastal environment. If the environmental systems degrade tourism will not sustain any more. However, tourism can provide incentive for the conservation and restoration of the natural environment. Nearly half of the respondents stated that because of tourism, natural environment is in better condition in Cox’s Bazar and tourism provides incentive for the conservation and restoration of the natural environment. The analysis also identifies that policies and strategies play a big role to the conservation of natural environment and resources, and the implementation of principles of sustainable development. Government of Bangladesh (GoB) has been formulated some policies and strategies related to tourism development, sustainable development and coastal zone management, but most of the policies and strategies are not implemented properly because of institutional conflicts. In the light of the data analysis, discussion and findings, some recommendations are suggested here to help and guide future decisions regarding tourism development, conservation of the environment, sustainable development and sustainability at coastal areas in Bangladesh. / published_or_final_version / Urban Planning and Design / Master / Master of Science in Urban Planning

Doctor of Philosophy(PhD) / This thesis employs survival analysis framework (Allison, 1984) and the Cox’s hazard model (Cox, 1972) to estimate the probability that a credit rating survives in its current grade at a certain forecast horizon. The Cox’s hazard model resolves some significant drawbacks of the conventional estimation approaches. It allows a rigorous testing of non-Markovian behaviours and time heterogeneity in rating dynamics. It accounts for the changes in risk factors over time, and features the time structure of probability survival estimates. The thesis estimates three stratified Cox’s hazard models, including a proportional hazard model, and two dynamic hazard models which account for the changes in macro-economic conditions, and the passage of survival time over rating durations. The estimation of these stratified Cox’s hazard models for downgrades and upgrades offers improved understanding of the impact of rating history in a static and a dynamic estimation framework. The thesis overcomes the computational challenges involved in forming dynamic probability estimates when the standard proportionality assumption of Cox’s model does not hold and when the data sample includes multiple strata. It is found that the probability of rating migrations is a function of rating history and that rating history is more important than the current rating in determining the probability of a rating change. Switching from a static estimation framework to a dynamic estimation framework does not alter the effect of rating history on the rating migration hazard. It is also found that rating history and the current rating interact with time. As the rating duration extends, the main effects of rating history and current rating variables decay. Accounting for this decay has a substantial impact on the risk of rating transitions. Downgrades are more affected by rating history and time interactions than upgrades. To evaluate the predictive performance of rating history, the Brier score (Brier, 1950) and its covariance decomposition (Yates, 1982) were employed. Tests of forecast accuracy suggest that rating history has some predictive power for future rating changes. The findings suggest that an accurate forecast framework is more likely to be constructed if non-Markovian behaviours and time heterogeneity are incorporated into credit risk models.

Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.

Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.

No description available.

Made available in DSpace on 2016-06-02T20:06:00Z (GMT). No. of bitstreams: 1 1662.pdf: 571364 bytes, checksum: 6091268473b4a7cb920748fd364c2a99 (MD5) Previous issue date: 2007-03-30 / Survival data analysis models is used to study experimental data where, normally, the variable "answer"is the time passed until an event of interest. Many authors do prefer modeling survival data, in the presence of co-variables, by using a hazard function - which is related with its interpretation. The Cox model (1972) - most commonly used by the authors - is applicable when the fail rates are proportional. This model is very flexible and used in the survival analysis. It can be easily extended to, for example, incorporate the time-dependent co-variables. In the present work we propose a proportional risk model which incorporates a time-dependent parameter named "time-dependent proportional risk model". / A análise de sobrevivência tem por objetivo estudar dados de experimento em que a variável resposta é o tempo até a ocorrência de um evento de interesse. Vários autores têm preferido modelar dados de sobrevivência na presença de covariáveis por meio da função de risco, fato este relacionado à sua interpretação. Ela descreve como a probabilidade instantânea de falha se modifca com o passar do tempo. Nesse contexto, um dos modelos mais utilizados é o modelo de Cox (Cox, 1972), onde a suposição básica para o seu uso é que as taxas de falhas sejam proporcionais. O modelo de riscos proporcionais de Cox é bastante flexível e extensivamente usado em análise de sobrevivência. Ele pode ser facilmente estendido para incorporar, por exemplo, o efeito de covariáveis dependentes do tempo. Neste estudo, propõe-se um modelo de risco proporcional, que incorpora um parâmetro dependente do tempo, denominado modelo de risco proporcional dependente do tempo. Uma análise clássica baseada nas propriedades assintóticas dos estimadores de máxima verossimilhança dos parâmetros envolvidos é desenvolvida, bem como um estudo de simulação via técnicas de reamostragem para estimação intervalar e testes de hipóteses dos parâmetros do modelo. É estudado o custo de estimar o efeito da covariável quando o parâmetro que mede o efeito do tempo é considerado na modelagem. E, finalizando, apresentamos uma abordagem do ponto de vista Bayesiano.