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Survival Probability and Intensity Derived from Credit Default Swaps A Directed Research Project Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements
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How to fill out survival probability and intensity?

01
Determine the time period: To fill out survival probability and intensity, you first need to specify the time period for which you want to calculate these measures. This can be a specific time frame like one year or a longer period such as five years.
02
Collect relevant data: Next, gather all the necessary data for your analysis. This may include information about the population or sample under study, the number of events (such as deaths or failures) that occurred during the given time period, and any additional relevant variables.
03
Calculate survival probability: To calculate survival probability, you can use different statistical methods depending on the nature of your data. One commonly used method is the Kaplan-Meier estimator, which takes into account the observed survival times and censoring information to estimate the survival function.
04
Calculate intensity: Intensity refers to the rate at which events occur during a specific time interval. To calculate the intensity rate, you need to divide the number of events that occurred within the time interval by the corresponding length of that interval. This can be done using appropriate mathematical formulas or statistical techniques.

Who needs survival probability and intensity?

01
Researchers and scientists: Survival probability and intensity are crucial measures in various fields of research, including medicine, epidemiology, engineering, and finance. Researchers use these measures to analyze and interpret survival data, study disease progression, assess product reliability, and make informed decisions based on the likelihood of survival or failure.
02
Healthcare professionals: Survival probability and intensity can be essential for healthcare professionals when assessing patient prognosis, determining treatment effectiveness, and predicting disease outcomes. These measures assist in identifying high-risk individuals or groups and guide treatment strategies accordingly.
03
Insurance and finance professionals: Survival probability and intensity are also relevant in insurance and finance industries. Insurers utilize these measures to estimate potential risks, determine premium rates, and manage longevity risk in life insurance policies. In finance, survival probability and intensity help assess credit risk, evaluate investments, and estimate default probabilities.
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Survival probability and intensity refers to the likelihood of an event or condition affecting the survival of a particular entity or system. It is often used in actuarial science and risk management to assess and analyze potential risks and their impact on longevity or sustainability.
The requirement to file survival probability and intensity may vary depending on the specific jurisdiction and industry. Generally, it is associated with companies, organizations, or individuals involved in insurance, pension funds, or actuarial practices.
Filling out survival probability and intensity typically involves collecting relevant data regarding the entity or system under study, including demographic information, historical records, and relevant statistical models. This data is used to calculate and assess the probabilities and intensities related to the entity's or system's survival or longevity.
The purpose of survival probability and intensity is to provide quantitative measures that help assess and manage risks related to the survival or longevity of a particular entity or system. It can be used for various purposes, such as financial planning, actuarial analysis, risk assessment, and evaluating the viability of insurance or pension schemes.
The information reported on survival probability and intensity may depend on the specific requirements of the jurisdiction or industry. Typically, it includes relevant demographic data (e.g., age, gender), historical mortality or failure rates, statistical models used for analysis, and any assumptions or factors considered in the calculations.
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