Scaling lognormal distribution by exponential function

In summary, the distribution resulting from scaling a lognormal distribution by a non-constant exponential function is still a lognormal distribution, but with different mean and variance values.
  • #1
Byronin
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I am multiplying a lognormal distribution by an function to scale it larger. While I know that scaling a lognormal distribution by a constant multiplier yields a lognormal distribution, in this case the multiplier is not a constant. Instead, smaller values from the lognormal distribution are multiplied by smaller values of the exponential function; and larger values from the lognormal distribution are multiplied by larger values of the exponential function. What is the distribution that results from this transformation? also lognormal?
 
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  • #2
The distribution resulting from this transformation is still a lognormal distribution. However, the mean and variance of the new lognormal distribution will be different than that of the original lognormal distribution.
 

FAQ: Scaling lognormal distribution by exponential function

What is the purpose of scaling a lognormal distribution by an exponential function?

The purpose of scaling a lognormal distribution by an exponential function is to transform the data so that it follows a normal distribution. This can be useful in statistical analysis as normal distributions are easier to work with and have well-defined properties.

How does scaling affect the shape of a lognormal distribution?

Scaling a lognormal distribution by an exponential function changes the shape of the distribution from a skewed, right-tailed distribution to a symmetrical, bell-shaped distribution. This transformation is achieved by taking the logarithm of the data and applying an exponential function, which compresses the data on the right side and stretches it on the left side.

What is the relationship between the parameters of a lognormal distribution and the scaling factor?

The parameters of a lognormal distribution, namely the mean and standard deviation, have a direct relationship with the scaling factor. As the scaling factor increases, the mean of the lognormal distribution also increases, while the standard deviation decreases. This relationship can be mathematically described by the formula: lognormal distribution = exp(mean + scaling factor * standard deviation).

Can a lognormal distribution be scaled by any exponential function?

Yes, a lognormal distribution can be scaled by any exponential function as long as the function is strictly increasing. This means that the function must have a positive slope, which ensures that the data is compressed and stretched in the desired direction. Common examples of exponential functions used for scaling include the natural logarithm, base 10 logarithm, and the exponential function itself.

How can scaling by an exponential function be used in practical applications?

Scaling by an exponential function is commonly used in various fields such as finance, economics, and biology. One practical application is in finance, where lognormal distributions are often used to model stock prices. By scaling the lognormal distribution with an exponential function, analysts can better understand the behavior of stock prices and make more accurate predictions. In biology, scaling can be used to transform skewed data from experiments into a normal distribution, making it easier to analyze and draw conclusions from the data.

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