Simulating the Pareto Distribution with Inverse Transform Sampling скачать в хорошем качестве

Simulating the Pareto Distribution with Inverse Transform Sampling

Simulating the Pareto Distribution with Inverse Transform Sampling The Pareto distribution, unlike some other distributions, doesn't have a built-in function in R for generating random variables. Therefore, one must write custom code to simulate variables from this distribution. This involves implementing a function named `rpareto` that computes values based on the specified probability density function. In the exercise, the task is to use this custom `rpareto` function to simulate 1,000 values from a Pareto distribution with parameters α = 3 and λ = 1. These simulated values are then stored in a vector called `LNorm_Vector`. Subsequently, the mean and variance of the values in this vector are calculated to understand the statistical properties of the simulated data. Overall, the exercise combines theoretical understanding with practical implementation. By writing custom R code and conducting simulations, one gains insights into the behavior and characteristics of the Pareto distribution, which is essential for various applications in fields such as economics and finance. This exercise refers to the Pareto Type II distribution, also known as the Lomax distribution