A detailed discussion of the mathematical properties of lognormal distribution is found in this previous post in a companion blog. This post shows how to work basic calculation problems for lognormal distribution. A summary of lognormal distribution is given and is followed by several examples. Practice problems are in the next post.
The random variable is said to follow a lognormal distribution with parameters and if follows a normal distribution with mean and variance . Here, is the natural logarithm in base = 2.718281828…. It is difficult (if not impossible) to calculate probabilities by integrating the lognormal density function. Since the lognormal distribution is intimately related to the normal distribution, the basic lognormal calculation is performed by calculating the corresponding normal distribution. The following summary shows how.
In the following points, has a lognormal distribution with parameters and and is the corresponding normal distribution with mean and variance . The notation means raising to the number .
|1. Lognormal observations and normal observations|
|2. Lognormal CDF and normal CDF|
|3. Lognormal density function and normal density function|
|4. Lognormal moments and normal moment generating function|
|5. Examples of lognormal moments|
|6. Lognormal percentiles and normal percentiles|
|7. Constant multiple of lognormal distribution|
|8. Mode of lognormal distribution|
Two examples are given to illustrate the calculation discussed here. The next post has practice problems.
All normal probabilities are obtained by using the normal distribution table found here.
Suppose that the random variable has a lognormal distribution with parameters = 1 and = 2. Calculate the following.
- The 67th, 95th and 99th percentiles of .
- Let . Find and
To find the percentiles, first find the standard normal percentiles, either by using calculator or by looking up a table. Using a standard normal table, we get 0.44 (67th percentile), 1.645 (95th percentile) and 2.33 (99th percentile). The following gives the lognormal percentiles.
The random variable has a lognormal distribution with parameters and = 2.
Note. One interpretation of is that of inflation, in this case a 10% inflation. For example, let be the size of a randomly selected auto insurance collision claim in the current year. If the claims are expected to increase 10% in the following year, is the the size of a randomly selected claim in the following year.
Suppose that the random variable has a lognormal distribution with mean 12.18 and variance 255.02. Calculation the following.
- The skewness and kurtosis of .
First, determine the parameters and by setting up the following equations.
Plug the first equation into the second equation and obtain the equation . Solving for produces = 1. Plug = 1 into the first equation produces = 2. The following gives the desired probability.
To find the skewness and kurtosis, one way is to find the first 4 lognormal moments and then calculate the third standardized moment (skewness) and the fourth standardized moment (kurtosis). To see how this is done, see this previous post. Another is to use the formulas given above.
Suppose that the lifetime (in years) of a certain type of machines follows the lognormal distribution described in Example 2. Suppose that you purchased such a machine that is 10-year old. What is the probability that it will last another 10 years?
This is a conditional probability since the machine already survived for 10 years already.
2017 – Dan Ma
This post has two practice problems to complement the previous post Working with moments.
|Practice Problem 1a|
Losses are modeled by a distribution that is the independent sum of two exponential distributions, one with mean 6 and the other with mean 12.
|Practice Problem 1b|
Losses are modeled by a distribution that is a mixture of two exponential distributions, one with mean 6 and the other with mean 12. The weight of each distribution is 50%.
As the previous post Working with moments shows, this is primarily an exercise in finding moments. There is no need to first find the probability density function of the loss distribution. For more information on exponential distribution, see here.
2017 – Dan Ma
This post gives some background information on calculation involving moments.
Let be a random variable. Let be its mean and be its variance. Thus is the standard deviation of .
The expectation is the th raw moment. It is also called the th moment about zero. The expectation is the th central moment. It is also called the th moment about the mean. Given , its standardized random variable is . Then the th standardized moment is .
The mean of is the first raw moment . The variance of is the second central moment . It is equivalent to . In words, the variance is the second raw moment subtracting the square of the mean.
The skewness of is the third standardized moment of . The kurtosis is the fourth standardized moment of . The excess kurtosis is the kurtosis subtracting 3. They are:
The calculation is usually done by expanding the expression inside the expectation. As a result, the calculation would then be a function of the individual raw moments up to the third or fourth order.
Note that the last line in skewness is a version that depends on the mean, the variance and the third raw moment. The calculation is illustrated with some examples. Practice problems are given in subsequent posts.
Losses are modeled by a distribution that has the following density function. Calculate the mean, variance, skewness and kurtosis of the loss distribution.
The following shows the calculation of the first four raw moments.
The following shows the results.
2017 – Dan Ma