Bounding a truncated normal with a gamma

In summary, to find a Gamma distribution that is always above a truncated normal distribution, choose a value for A, the gamma's shape parameter, that makes its pdf diverge at x=0. Then use numeric analysis to solve for x and B in the equations f(x,A,B)=0 and ∂f/∂x(x,A,B)=0. Remove any solutions where the second derivative of f is less than or equal to 0. This will give you the parameters for curves that satisfy the desired property. You can repeat this process for different values of A to find additional solutions.
  • #1
FallenApple
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So say I have a truncated normal. That is, N(mu,sigma) that is from 0 to infinity only.

I need to find a Gamma such that a constant C*Gamma(A,B) is always above N(mu, sigma). How would I go about finding such a A, B that would work given fixed mu and sigma?
 
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  • #2
Choose a value of ##A##, the gamma's shape parameter. It will have to be a value that makes the gamma's pdf diverge to infinity at ##x=0##, because otherwise the pdf will be zero there, and so will be below the normal pdf.
Let ##f## be the pdf of the gamma minus the pdf of the truncated normal. It will be a function of ##x, A, B##, of which only two (##x, B##) are unknown.
Use numeric analysis to solve the following two equations for ##x## and ##B##.

1. ##f(x,A,B)=0##
2. ##\frac{\partial f}{\partial x}(x,A,B)=0##.

Remove any solutions where ##\frac{\partial^2 f}{\partial x^2}(x,A,B)\leq 0##.

What remains will be parameters of curves that have the property you seek. The gamma pdf curve will touch that of the normal, but not go below it. There may be more than one such curve.

You can repeat this for different admissible values of ##A## to get additional sets of solutions.
 
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Related to Bounding a truncated normal with a gamma

What is a truncated normal distribution?

A truncated normal distribution is a probability distribution that is restricted to a certain range or interval. It is similar to a normal distribution, but values outside of the specified range are set to 0.

What is a gamma distribution?

A gamma distribution is a continuous probability distribution that is commonly used to model waiting times or the amount of time until a certain event occurs. It is characterized by two parameters, shape and scale, and can take on a wide range of shapes depending on these parameters.

How can a truncated normal distribution be bounded by a gamma distribution?

A truncated normal distribution can be bounded by a gamma distribution by using the cumulative distribution function (CDF) of both distributions. The CDF of a truncated normal distribution is used to restrict the range of values, while the CDF of a gamma distribution is used to transform the values to fit the desired shape.

What are the advantages of bounding a truncated normal with a gamma distribution?

Bounding a truncated normal with a gamma distribution can provide a more flexible and realistic model for data that is restricted to a certain range. It also allows for easier interpretation of the data, as the parameters of the gamma distribution have clear interpretations (shape and scale).

What are some practical applications of bounding a truncated normal with a gamma distribution?

Bounding a truncated normal with a gamma distribution can be useful in various fields such as finance, engineering, and social sciences. It can be used to model waiting times, durations, or any other variable that is restricted to a specific range of values.

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