Maximum Likelihood Estimator for Exponential Density Function

In summary, the given problem involves determining the value of c as a function of λ and finding the maximum likelihood estimator of λ. The approach involves integrating from 0 to infinity, using substitution and integration by parts. The final solution involves differentiating with respect to λ.
  • #1
twoski
181
2

Homework Statement



Given f(x;λ) = [itex]cx^{2}e^{-λx}[/itex] for x ≥ 0

Determine what c must be (as a function of λ) then determine the maximum likelihood estimator of λ.

The Attempt at a Solution



So I'm supposed to integrate this from 0 to infinity, from what i can gather.

Let u = [itex]x^{2}[/itex], du = 2xdx, dv = [itex]e^{-λx}[/itex] and v = [itex]-e^{-λx} / λ[/itex]

After a bit of work i end up with:

-c/λ [ [itex]x^{2}e^{-λx}|_{0}^{∞} + 2( xe^{-λx}/λ |^{∞}_{0})[/itex] ]

What throws me off is that evaluating this leaves me with -c/λ( 0 ), which has to be wrong...
 
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  • #2
twoski said:

Homework Statement



Given f(x;λ) = [itex]cx^{2}e^{-λx}[/itex] for x ≥ 0

Determine what c must be (as a function of λ) then determine the maximum likelihood estimator of λ.

The Attempt at a Solution



So I'm supposed to integrate this from 0 to infinity, from what i can gather.

Let u = [itex]x^{2}[/itex], du = 2xdx, dv = [itex]e^{-λx}[/itex] and v = [itex]-e^{-λx} / λ[/itex]

After a bit of work i end up with:

-c/λ [ [itex]x^{2}e^{-λx}|_{0}^{∞} + 2( xe^{-λx}/λ |^{∞}_{0})[/itex] ]

What throws me off is that evaluating this leaves me with -c/λ( 0 ), which has to be wrong...

You have to integrate the second term from 0 to infinity, not just evaluate it. You'll need to integrate by parts again.
 
  • #3
twoski said:

Homework Statement



Given f(x;λ) = [itex]cx^{2}e^{-λx}[/itex] for x ≥ 0

Determine what c must be (as a function of λ) then determine the maximum likelihood estimator of λ.

The Attempt at a Solution



So I'm supposed to integrate this from 0 to infinity, from what i can gather.

Let u = [itex]x^{2}[/itex], du = 2xdx, dv = [itex]e^{-λx}[/itex] and v = [itex]-e^{-λx} / λ[/itex]

After a bit of work i end up with:

-c/λ [ [itex]x^{2}e^{-λx}|_{0}^{∞} + 2( xe^{-λx}/λ |^{∞}_{0})[/itex] ]

What throws me off is that evaluating this leaves me with -c/λ( 0 ), which has to be wrong...

It might be easier to recognize that
[tex] x e^{-\lambda x} = - \frac{\partial}{\partial \lambda} e^{- \lambda x}, [/tex]
and so forth.
 

1. What is an exponential density function?

An exponential density function is a type of probability distribution that models the behavior of a continuous random variable. It is often used to describe the amount of time it takes for an event to occur, such as the time between phone calls or the time between radioactive decays.

2. How is an exponential density function calculated?

The exponential density function is calculated using the formula f(x) = λe-λx, where λ is the rate parameter and x is the value of the random variable. This formula represents the probability that the random variable will take on a specific value.

3. What is the relationship between the rate parameter and the shape of the exponential density function?

The rate parameter, λ, determines the shape of the exponential density function. A higher value of λ results in a steeper curve, while a lower value of λ leads to a flatter curve. The larger the value of λ, the more likely it is for the event to occur in a shorter amount of time.

4. How is an exponential density function used in real-world applications?

An exponential density function is commonly used in fields such as finance, engineering, and biology to model the behavior of continuous random variables. It can be used to predict the likelihood of future events based on past data, such as predicting the time between customer purchases or the lifespan of a product.

5. What is the difference between an exponential density function and a normal distribution?

An exponential density function is a type of skewed distribution, meaning that it is not symmetrical around the mean. In contrast, a normal distribution is a symmetrical bell-shaped curve. Additionally, the exponential density function is used to model the behavior of continuous random variables, while the normal distribution is used for both continuous and discrete variables.

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