Weibull Distribution Homework: Generating Random Observations from Weibull(k,λ)

In summary, the Weibull distribution is commonly used in survival analysis and can be parameterized with shape and scale parameters. It has been shown to be a PDF, and the CDF, median value, variance, and failure rate have been found. To generate random observations from a Weibull distribution, a suitable function can be used with a Uniform[0,1] random variable. One possible function is the CDF of the Weibull distribution, which can be manipulated using the property that if ##U## is Uniform, then so is ##1-U##.
  • #1
squenshl
479
4

Homework Statement


The Weibull distribution is used frequently as a lifetime distribution and is so is used a lot in survival analysis. It can be parameterised as:##f(x;\lambda,k) = \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^k}## for ##x\geq 0## & ##0## for ##x < 0##, where ##k > 0## is called the shape parameter and ##\lambda > 0## is called the scale parameter of the distribution.

I have shown that it is a PDF, found the CDF, median value, Variance & failure rate. My question is how would I describe how you would generate random observations from a ##\text{Weibull}(k,\lambda)## distribution from random ##\text{Uniform}(0,1)## observations.

Homework Equations

The Attempt at a Solution


I don't even know where to begin. Please help.
 
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  • #2
Hmm. This is something that would usually just be taught, rather than asking the student to derive it, as the answer is not terribly obvious.
I'll try to give a hint that doesn't give the whole thing away.
The desired random variable W will be a function of U where U is a Uniform[0,1] random variable.
Can you think of a suitable function to use that will have the desired properties, given the functions you have worked out above and their properties, including domain and range?
 
  • #3
squenshl said:

Homework Statement


The Weibull distribution is used frequently as a lifetime distribution and is so is used a lot in survival analysis. It can be parameterised as:##f(x;\lambda,k) = \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^k}## for ##x\geq 0## & ##0## for ##x < 0##, where ##k > 0## is called the shape parameter and ##\lambda > 0## is called the scale parameter of the distribution.

Homework Equations

The Attempt at a Solution


I don't even know where to begin. Please help.

Google "Generation of random variable" or "...random variate".
 
  • #4
andrewkirk said:
Hmm. This is something that would usually just be taught, rather than asking the student to derive it, as the answer is not terribly obvious.
I'll try to give a hint that doesn't give the whole thing away.
The desired random variable W will be a function of U where U is a Uniform[0,1] random variable.
Can you think of a suitable function to use that will have the desired properties, given the functions you have worked out above and their properties, including domain and range?
Oh right. I used the CDF of the Weibull distribution and noted that if ##U## is Uniform, then so is ##1-U## to get the desired result.
 

What is the Weibull distribution?

The Weibull distribution is a continuous probability distribution that models the time to failure of an object or system. It is commonly used in reliability engineering and survival analysis.

What is the formula for the Weibull distribution?

The formula for the Weibull distribution is f(x) = (k/λ) * (x/λ)(k-1) * e-(x/λ)k, where k is the shape parameter and λ is the scale parameter.

How do I generate random observations from the Weibull distribution?

To generate random observations from the Weibull distribution, you can use a computer program or statistical software such as R or Python. These programs have built-in functions that allow you to input the shape and scale parameters and generate a specified number of random observations from the distribution.

What is the significance of the shape and scale parameters in the Weibull distribution?

The shape parameter, k, determines the shape of the distribution curve. A higher value of k indicates a shorter time to failure and a lower value indicates a longer time to failure. The scale parameter, λ, determines the scale or location of the distribution. A higher value of λ indicates a larger spread of the distribution and a lower value indicates a smaller spread.

Can the Weibull distribution be used for any type of data?

The Weibull distribution is commonly used for modeling time to failure data, but it can also be used for other types of continuous data. However, it is important to check if the data follows a Weibull distribution before using it in analysis.

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