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ASC550 Survival Models UITM Assignment Answer Malaysia 

ASC550 Survival Models is a course offered at UITM (Universiti Teknologi MARA) in Malaysia. This course is designed to provide students with a solid foundation in the fundamentals and application of survival models, as well as their analysis. It covers topics that align with the syllabus of Core Statistics 2 (CS2) from the Institute and Faculty of Actuaries (IFoA).

Throughout the course, students will explore concepts such as survival functions, hazard functions, censoring, and truncation.We will also learn how to estimate parameters, interpret model results, and validate the adequacy of survival models. Practical applications and case studies will be incorporated to enhance understanding and demonstrate the real-world relevance of survival models.

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Assignment Brief 1 : Describe techniques of survival analysis to actuarial related problems

Survival analysis is a statistical method used in actuarial science to analyze and model time-to-event data. It is particularly useful in analyzing insurance-related problems where the occurrence of events, such as death, illness, or policy termination, is of interest. 

Here are some techniques commonly used in survival analysis for actuarial problems:

  • Kaplan-Meier Estimator: The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from censored data. It provides an estimate of the probability that an event has not occurred up to a specific time point.
  • Cox Proportional Hazards Model: The Cox proportional hazards model is a widely used semi-parametric model in survival analysis. It allows for the analysis of the effect of covariates on the hazard rate, which is the instantaneous probability of an event occurring at a given time.
  • Accelerated Failure Time Models: Accelerated failure time models assume that the logarithm of the survival time has a linear relationship with the covariates. These models are useful when the proportional hazards assumption of the Cox model is violated.
  • Parametric Survival Models: Parametric survival models assume a specific distribution for the survival time. Examples include the exponential, Weibull, and log-normal distributions. These models can provide more precise estimates if the distributional assumptions are met.
  • Competing Risks Analysis: Competing risks analysis deals with situations where individuals may experience different types of events, and the occurrence of one event may preclude the occurrence of others. This technique allows for the estimation of cumulative incidence functions for multiple competing events.
  • Time-Varying Covariates: Survival models can be extended to include covariates that change over time. These time-varying covariates can capture dynamic changes in risk factors and improve the model’s predictive ability.

Assignment Brief 2 : Apply techniques of survival analysis to solve actuarial problems

For this assignment, you are required to apply survival analysis techniques to solve actuarial problems. You can choose a specific actuarial problem related to insurance, mortality, or policy termination and utilize appropriate survival analysis techniques to analyze the time-to-event data. Here is a suggested approach:

  • Data Collection: Gather relevant data related to the actuarial problem you have chosen. This may include information on individuals, events, censoring, and covariates.
  • Data Preprocessing: Clean and prepare the data for analysis. Handle missing values, format the variables correctly, and ensure the data is in the appropriate structure for survival analysis.
  • Descriptive Analysis: Conduct exploratory data analysis to understand the characteristics of the data. Calculate summary statistics, create Kaplan-Meier survival curves, and examine the relationships between variables.
  • Model Selection: Choose an appropriate survival analysis model based on the nature of the problem and the data. Consider whether the Cox proportional hazards model, accelerated failure time model, or a parametric survival model would be most suitable.
  • Model Estimation: Estimate the parameters of the selected survival model using maximum likelihood estimation or other appropriate techniques.
  • Model Evaluation: Assess the goodness of fit of the model and examine the assumptions. Use techniques such as residual analysis, log-likelihood ratio tests, and graphical methods to evaluate the model’s performance.
  • Interpretation and Inference: Interpret the estimated coefficients and hazard ratios to understand the impact of covariates on the event of interest. Make inferences about the risk factors and their effects on the outcome.
  • Sensitivity Analysis: Perform sensitivity analyses to assess the robustness of the results and investigate the impact of potential confounders or influential observations.
  • Report and Conclusion: Summarize your findings, present the results in a clear and concise manner, and draw conclusions based on the analysis.

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Assignment Brief 3 : Demonstrate lifelong learning skills in project assignments related to survival models

In this assignment, you will demonstrate your lifelong learning skills by working on project assignments related to survival models. Lifelong learning involves the continuous acquisition of knowledge and skills throughout one’s life. Here are some steps to follow:

  • Project Selection: Choose a project related to survival models that aligns with your learning goals and interests. It could involve exploring a specific application area, comparing different survival analysis techniques, or addressing a particular research question.
  • Research and Learning: Engage in independent research to gather relevant literature and resources related to your chosen project. Read academic papers, textbooks, online tutorials, or attend webinars to deepen your understanding of survival models and their applications.
  • Project Planning: Develop a detailed project plan outlining the objectives, tasks, and timeline for completion. Break down the project into manageable components and allocate sufficient time for research, implementation, and analysis.
  • Implementation and Analysis: Apply the survival analysis techniques discussed in Assignment Brief 1 to your project. Clean and preprocess the data, select appropriate models, estimate parameters, and perform necessary analyses to address the research question or problem statement.
  • Documentation: Maintain a comprehensive record of your work, including code, data, and analysis results. Document your methodology, assumptions, and any challenges or modifications encountered during the project.
  • Reflection and Improvement: Reflect on the project outcomes and identify areas for improvement. Consider the limitations of the techniques used, potential biases, and alternative approaches that could have been employed. Reflecting on your work will help refine your understanding and enhance your future learning.
  • Presentation and Sharing: Prepare a clear and concise report summarizing your project findings and present your results to peers, instructors, or other relevant audiences. Share your insights, lessons learned, and potential applications of the survival models in actuarial science.

By following these steps and actively engaging in the project assignments, you will demonstrate your lifelong learning skills and enhance your understanding of survival models in actuarial contexts.

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