How Do You Convert Between HT(t), FT(t), and fT(t) in Probability Calculations?

  • Thread starter Thread starter estado3
  • Start date Start date
Click For Summary
SUMMARY

This discussion focuses on converting between the hazard function (HT(t)), cumulative distribution function (FT(t)), and density function (fT(t)) in probability calculations. The hazard function is defined as λ(t) = f(t)/(1 - F(t)), where f(t) is the density function and F(t) is the cumulative distribution function. The cumulative hazard function is expressed as Λ(t) = -log(1 - F(t)), and its derivative gives the hazard function. The discussion also highlights the relationship between these functions through differential equations, particularly in the context of the uniform distribution.

PREREQUISITES
  • Understanding of probability theory and functions
  • Familiarity with cumulative distribution functions (CDF)
  • Knowledge of density functions (PDF)
  • Basic calculus, specifically differential equations
NEXT STEPS
  • Study the derivation of the hazard function from the density function using examples
  • Learn about the cumulative hazard function and its applications in survival analysis
  • Explore the relationship between different probability distributions and their functions
  • Practice solving first-order differential equations relevant to probability functions
USEFUL FOR

Students and professionals in statistics, data science, and actuarial science who need to understand the relationships between different probability functions, particularly in the context of survival analysis and risk assessment.

estado3
Messages
13
Reaction score
0

Homework Statement



Looking for a step by step online guide/tutorial/worked example showing equations for getting to the hazard function from the density function, the cumulative distribution function from the hazard function, and vice versa

Homework Equations



HT(t) = hazard function
FT(t) = Cumulative distribution function
fT(t) = Density function

The Attempt at a Solution

 
Physics news on Phys.org
Distribution function and density function I know but I had to look up "Hazard function".
According to Wikipedia,
http://en.wikipedia.org/wiki/Survival_analysis
If we have the cumulative distribution function, [/itex]F(t)= Pr(T\le t)[/itex], we define S(t)= 1- T(t). Then the Hazard function, \lambda(t), is given by
\lambda = -\frac{S'(t)}{S(t)}
Directly in terms of F, then, since S'(t)= (1- F(t))'= -F'(t),
\lambda = \frac{F'(t)}{1- F(t)}

If f(t) is the density function, f(t)= F'(t), then
\lambda = \frac{f(t)}{1- F(t)}

Alternatively, we can define the "cumulative hazard function", \Lambda(t)= -log(1- F(t)) and then the hazard function is the derivative: \lambda(t)= d \Lambda(t)/dt
In any case, finding \lambda involves solving a first order differential equation.

To take a simple example, the uniform distribution from 0 to 1, the density function is a constant, f(x)= 1, so F(x)= \int_0^t 1 dx= t and the hazard function is given by \lambda(t)= f(t)/(1- F(t))= 1/(1- t). Alternatively, the cumulative hazard function is \Lamba(t)= -log(F(t))= -log(1-t) and the hazard function is the derivative of that: \lambda(t)= d(-log(1-t))/dt= 1/(1-t).

Going the other way, if we were given \lambda(t)= 1/(1- t), then \lambda(t)= F'/(1- F)= 1/(1- t) so finding \lambda(t) requires solving a differential equation: F'= \lambda(t)(1- F)= (1- F)/(1- t). That's a "separable" differential equation: dF/(1- F)= dt/(1-t) . Integrating, log(1- F(t))= log(1- t)+ C1 so 1- F(t)= C2(1- t). In order that F(0)= 0, we must have C2= 1 so 1- F(t)= 1- t and F(t)= t as before.
 
thanks for the link, I am wondering at what stage we use the hazard function, I understand the p.d.f is used for a moment in time, while the c.d.f is used for a time period i.e 0< = T, at what stage do we need the hazard function?
 

Similar threads

Replies
1
Views
1K
  • · Replies 28 ·
Replies
28
Views
4K
  • · Replies 24 ·
Replies
24
Views
5K
  • · Replies 5 ·
Replies
5
Views
4K
  • · Replies 9 ·
Replies
9
Views
8K
  • · Replies 10 ·
Replies
10
Views
2K
  • · Replies 11 ·
Replies
11
Views
2K
Replies
4
Views
16K
Replies
2
Views
1K
  • · Replies 35 ·
2
Replies
35
Views
6K