Survival Analysis – Reliability Theory / Human Life – Transistor’s Life (Part 3)

In continuation with yesterday’s discussion, we saw that human life is marked by several random events. The time of marriage is a continuous random variable as it can take any value in a real continuum. Similarly the number of children born to the individual is a discrete random variable denoted by say n. Here n = 0, 1, 2, 3, …., m; here n = 0 denotes having no children, n = 1 denotes having a child in the entire life time of an individual and so on. Similarly the time of birth of first child is a continuous random variable, the time of birth of second child is also a continuous random variable and so is the time between two consecutive births. These are continuous random variables as time is continuous and can take any value in the continuum. But the time of second birth is greater than the time of first birth. The time at death is a continuous random variable and the age at the death of an individual is a continuous random variable. The number of professional jobs taken by an individual in his entire life time is a discrete random variable. We see that human life is governed by multiple discrete and continuous random variables, which take values governed by some probability law.  Our objective is to explain this probability law with the help of a function. This way we can quantify the chances of occurrences of these events. In survival analysis we are interested in predicting the mortality of an individual. This is very useful in actuarial science which is based on quantitative value of chance of mortality of an individual.