Predictive Inference with Copulas for Bivariate Data

Nonparametric predictive inference (NPI) is a statistical approach with strong frequentist properties, with inferences explicitly in terms of one or more future observations. NPI is based on relatively few modelling assumptions, enabled by the use of lower and upper probabilities to quantify uncertainty. While NPI has been developed for a range of data types, and for a variety of applications, thus far it has not been developed for multivariate data. This thesis presents the rst study in this direction. Restricting attention to bivariate data, a novel approach is presented which combines NPI for the marginals with copulas for representing the dependence between the two variables. It turns out that, by using a discretization of the copula, this combined method leads to relatively easy computations. The new method is introduced with use of an assumed parametric copula. The main idea is that NPI on the marginals provides a level of robustness which, for small to medium-sized data sets, allows some level of misspecication of the copula. As parametric copulas have restrictions with regard to the kind of dependency they can model, we also consider the use of nonparametric copulas in combination with NPI for the marginals. As an example application of our new method, we consider accuracy of diagnostic tests with bivariate outcomes, where the weighted combination of both variables can lead to better diagnostic results than the use of either of the variables alone. The results of simulation studies are presented to provide initial insights into the performance of the new methods presented in this thesis, and examples using data from the literature are used to illustrate applications of the methods. As this is the rst research into developing NPI-based methods for multivariate data, there are many related research opportunities and challenges, which we briefly discuss

The transmission dynamic of the COVID 19 outbreak: A predictive dashboard

COVID 19 outbreak gives a great impact worldwide. The disaster of this pandemic has resulted in a large number of human lives being lost. As all countries implemented quarantine and social distancing, the great lockdown all over the world lead to multiple crises including health, economy, financial, and collapse in industrial and educational activities. Movement Control Order (MCO) and social distancing which have been implemented as control measures in Malaysia also affected many sectors. The landscape now has successfully reduced the number of infected people. However, from the economic point of view, the Retail Group Malaysia (RGM) has projected the country’s retail industry suffers a negative growth rate for the first time in 22 years. If the epidemic continues, society will reach an impasse, a time when the lockdown will become more than some of them can tolerate. As recognized by the World Health Organization (WHO), modelling the outbreak based on the prior input data is more appropriate than the ‘risk of bias’ for decision-makers. Thus, this research is conducted to model the outbreak of the disease using the susceptible-infected-recovery-death (SIRD) compartmental model accompanying with the varying infection rate due to changes in MCO measures. The model assumes under the unavailability of the vaccine, recovered people can be reinfected. The epidemic parameters and reproduction numbers are estimated and fitted from the transmission model to the actual data using the Monte Carlo Markov Chain (MCMC) of Metropolis-Hasting. The model is solved using a numerical algorithm of the Runge-Kutta method. The predictive dashboard of a graphical user interface (GUI) is developed, hence monitoring and predicting the outbreak under the control measures of the two different types of MCO scenarios (which are called constant and alternate scenarios) can be performed. GUI for the dynamic transmission of the COVID 19 provides insight for the future outbreak, hence may help the respective stakeholders to propose the best policy of a new norm for all sectors. From the GUI, we can see that, when no or loose MCO is implemented or compliance of the public to the COVID 19 standard operating procedure (SOP), the infected case will increase rapidly up to 7.5 million. With strict MCO regulation or public obedient to the SOP, the infected case will decrease rapidly, but even after a long period of strict regulation, once the quarantine is stopped, the infected case will rise again. An alternative MCO scenario is suggested where a cyclic pattern of strict and loose MCO regulation is upheld, and it shows to flatten the curve while allow periods of less restricted lifestyle. This can be one of the alternatives to balance the life and livelihood

New tourism product forecasting - A study of different potential markets

Mushrooming of new tourism products in Malaysia and their shutdown in the short term are the sign of ineffective forecast. Besides, no new development in tourism products causes the decline of the number of visitors to the attractions. This study proposes an application of a grey Bass diffusion model to forecast the new tourism product using different market potential value. A potential market can provide valuable information for a good forecast. From the managements’ perspective, proper planning and development is done to maintain the places based on the forecast. Grey Bass diffusion model, a popular model in handling forecast of a new product with limited data, is expected to forecast two ecotourism resorts, Tanah Aina Fahad and Tanah Aina Farrah Soraya using different market potential value. Yearly data from 2014 until 2018 from both tourism products are collected. The values of potential markets are calculated from the percentage number of visitors to Raub district since the new tourism products are located in Raub, Pahang. This study indicates that different market potential give significant impact on the accurate forecast. The closer the value of potential market calculated from percentage to the actual value, the higher the accuracy of the forecast

Kajian profil pencapaian matematik pelajar-pelajar sekolah rendah luar bandar dengan prestasi hati-HRV

Pembelajaran Matematik adalah asas kepada penguasan pembelajaran. Terdapat pelbagai punca kelemahan pelajar sekolah rendah dalam penguasaan Matematik. Salah satu ciri keupayaan pelajar dalam penguasaan pembelajaran adalah keupayaan mereka mengawal koheren “Heart Rate Variability – HRV” hati. Pengawalan koheren HRV membolehkan individu mengawal minda dan emosi seterusnya merangsang keupayaan diri untuk belajar dengan lebih berkesan. Kajian ini dilaksanakan untuk mengkaji sejauhmana pelajar kelainan penguasaan Matematik berupaya mengawal koheren hati. Adakah terdapat perbezaan skor koheren antara pelajar yang baik dan pelajar yang lemah dalam penguasaan Matematik. Kajian ini dijalankan melibatkan 50 orang sampel dalam dua kumpulan pelajar berbeza pencapaian, baik dan lemah. Pelajar diberikan beberapa latihan Matematik untuk meneliti adakah terdapat perbezaan skor koheren hati semasa mereka menjawab soalan Matematik. Dapatan menunjukkan terdapat perbezaan yang jelas antara kedua-dua kumpulan pelajar ini. Pelajar baik didapati berupaya mengawal koheren HRV dengan lebih baik berbanding pelajar yang lemah pencapaian Matematik. Dapatan menunjukkan skor yang berbeza antara kedua-dua kumpulan ini setelah melalui protokol pengujian yang berbeza. Dapatan merumuskan bahawa pelajar yang baik lebih berupaya mengawal skor koheren HRV berbanding pelajar yang lemah. Ini menunjukkan keupayaan pengawalan koheren adalah penting sebagai petunjuk pencapaian prestasi pelajar dalam pembelajaran Matematik. Kajian ini penting dalam proses pembelajaran kerana ia boleh membantu guru memperkenalkan latihan peningkatan koheren untuk membolehkan pelajar mengikuti pembelajaran Matematik dengan lebih baik. Teknik pengawalan koheren boleh dijadikan salah satu teknik untuk meningkatkan keberkesanan pembelajaran pelajar sekolah rendah

Modifiable risk factors and overall cardiovascular mortality: Moderation of urbanization

Background: Modifiable risk factors are associated with cardiovascular mortality (CVM) which is a leading form of global mortality. However, diverse nature of urbanization and its objective measurement can modify their relationship. This study aims to investigate the moderating role of urbanization in the relationship of combined exposure (CE) of modifiable risk factors and CVM. Design and Methods: This is the first comprehensive study which considers different forms of urbanization to gauge its manifold impact. Therefore, in addition to existing original quantitative form and traditional two categories of urbanization, a new form consisted of four levels of urbanization was duly introduced. This study used data of 129 countries mainly retrieved from a WHO report, Non-Communicable Diseases Country Profile 2014. Factor scores obtained through confirmatory factor analysis were used to compute the CE. Age-income adjusted regression model for CVM was tested as a baseline with three bootstrap regression models developed for the three forms of urbanization.Results: Results revealed that the CE and CVM baseline relationship was significantly moderated through the original quantitative form of urbanization. Contrarily, the two traditional categories of urbanization could not capture the moderating impact. However, the four levels of urbanization were objectively estimated the urbanization impact and subsequently indicated that the CE was more alarming in causing the CVM in levels 2 and 3 urbanized countries, mainly from low-middle-income countries.Conclusion: This study concluded that the urbanization is a strong moderator and it could be gauged effectively through four levels whereas sufficiency of two traditional categories of urbanization is questionable

Tourism demand forecasting – a review on the variables and models

With the growth of the world's tourism industry, researchers took advantage to conduct numerous studies in forecasting of tourism demand. The objective of this paper is to review the studies on tourism demand starting from 2010 to 2018 which varies on the explanatory variables, such as tourist income, exchange rate, gross domestic product, and others. In addition, this study also reviewed the models used to forecast and analyse tourism demand which are time-series model, econometric causal model and artificial intelligence model. The result from this review shows it is difficult to conclude which models performed the best for tourism demand. However, in most of the studies, combined models outperformed single model. Furthermore, the authors mentioned about the roles of tourism practitioners in the industry, tourism seasonality and suggestions for further studies in the future

Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading

COVID-19 pandemic was identified in Wuhan, China in 2019 and has spread at a tremendous rate affecting all countries over the world. Understanding the spreading disease is crucial; hence, the dynamic behaviour of the disease can be predicted. This paper is aimed to model the COVID-19 outbreak by extending the deterministic susceptible-infected-recovered-death (DSIRD) into a stochastic SIRD (SSIRD) model. Infectious rate parameter of the DSIRD model is perturbed with Brownian motion to reflect the uncertainty of the COVID-19 outbreak. Fourth order stochastic Runge-Kutta (SRK4) method is used to simulate the model. Parameter estimation is estimated using the Markov Chain Monte Carlo (MCMC) method. The simulated results for three ASEAN countries of Malaysia, Indonesia and Singapore indicate that SSIRD model is consistent with the infected COVID-19 data;hence, shows the model is adequate in explaining the behaviour of the infectious disease

Simulation of COVID-19 outbreaks via graphical user interface (GUI)

Background: This research aimed to model the outbreak of COVID-19 in Malaysia and develop a GUI-based model. Design and methods: The model is an improvement of the susceptible, infected, recovery, and death (SIRD) compartmental model. The epidemiological parameters of the infection, recovery, and death rates were formulated as time dependent piecewise functions by incorporating the control measures of lockdown, social distancing, quarantine, lockdown lifting time and the percentage of people who abide by the rules. An improved SIRD model was solved via the 4th order Runge-Kutta (RK4) method and 14 unknown parameters were estimated by using Nelder- Mead algorithm and pattern-search technique. The publicly available data for COVID-19 outbreak in Malaysia was used to validate the performance of the model. The GUI-based SIRD model was developed to simulate the number of active cases of COVID-19 over time by considering movement control order (MCO) lifted date and the percentage of people who abide the rules. Results: The simulator showed that the improved SIRD model adequately fitted Malaysia COVID-19 data indicated by low values of root mean square error (RMSE) as compared to other existing models. The higher the percentage of people following the SOP, the lower the spread of disease. Another key point is that the later the lifting time after the lockdown, the lower the spread of disease. Conclusions: These findings highlight the importance of the society to obey the intervention measures in preventing the spread of the COVID-19 disease

New tourism product forecasting – application of bass diffusion model and grey forecasting model

Previous researches usually applied Bass diffusion model (BDM) in forecasting the new product in various areas. This is the first application of BDM to the new tourism product since the model had been developed by Frank M. Bass in 1969. On the other hand, Grey forecasting model able to deal with limited number of data. Both BDM and grey forecasting model have been used in various areas in the forecasting studies. Taking advantages of both models, the combination of both Bass and grey model, called grey Bass forecasting model is applied in the context of the new tourism product forecasting. The objective of this study is to forecast the new tourism product demand in Malaysia using the developed model. Yearly visitors from two ecotourism resorts in Pahang, Tanah Aina Fahad and Tanah Aina Farrah Soraya from 2014 until 2018 are used. The results show that both BDM and grey Bass forecasting model are suitable in forecasting the new tourism product. The authors also suggest other factors affecting the attendance of visitors to be included in further research to conclude which model perform better in the future

The transmission dynamic of the COVID 19 outbreak : a predictive dashboard

COVID 19 outbreak gives a great impact worldwide. The disaster of this pandemic has resulted in a large number of human lives being lost. As all countries implemented quarantine and social distancing, the great lockdown all over the world lead to multiple crises including health, economy, financial, and collapse in industrial and educational activities. Movement Control Order (MCO) and social distancing which have been implemented as control measures in Malaysia also affected many sectors. The landscape now has successfully reduced the number of infected people. However, from the economic point of view, the Retail Group Malaysia (RGM) has projected the country’s retail industry suffers a negative growth rate for the first time in 22 years. If the epidemic continues, society will reach an impasse, a time when the lockdown will become more than some of them can tolerate. As recognized by the World Health Organization (WHO), modelling the outbreak based on the prior input data is more appropriate than the ‘risk of bias’ for decision-makers. Thus, this research is conducted to model the outbreak of the disease using the susceptible-infected-recovery-death (SIRD) compartmental model accompanying with the varying infection rate due to changes in MCO measures. The model assumes under the unavailability of the vaccine, recovered people can be reinfected. The epidemic parameters and reproduction numbers are estimated and fitted from the transmission model to the actual data using the Monte Carlo Markov Chain (MCMC) of Metropolis-Hasting. The model is solved using a numerical algorithm of the Runge-Kutta method. The predictive dashboard of a graphical user interface (GUI) is developed, hence monitoring and predicting the outbreak under the control measures of the two different types of MCO scenarios (which are called constant and alternate scenarios) can be performed. GUI for the dynamic transmission of the COVID 19 provides insight for the future outbreak, hence may help the respective stakeholders to propose the best policy of a new norm for all sectors. From the GUI, we can see that, when no or loose MCO is implemented or compliance of the public to the COVID 19 standard operating procedure (SOP), the infected case will increase rapidly up to 7.5 million. With strict MCO regulation or public obedient to the SOP, the infected case will decrease rapidly, but even after a long period of strict regulation, once the quarantine is stopped, the infected case will rise again. An alternative MCO scenario is suggested where a cyclic pattern of strict and loose MCO regulation is upheld, and it shows to flatten the curve while allow periods of less restricted lifestyle. This can be one of the alternatives to balance the life and livelihood