MLE of Poisson Dist: Find \lambda^2+1

In summary, the MLE for lambda squared plus one is found by taking the mean of the sample and squaring it, then adding one. This is the value of lambda squared plus one that maximizes the likelihood of the observed data.
  • #1
mrkb80
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Homework Statement


Let [itex] X_1,...,X_n [/itex] be a random sample from a poisson distribution with mean [itex]\lambda[/itex]

Find the MLE of [itex]\lambda^2 + 1 [/itex]

Homework Equations


The Attempt at a Solution



I found [itex]\hat{\lambda}=\bar{x}[/itex]

Can I just square it and add 1 and solve for lambda hat?

If not I have no idea how I would get the FOC (with respect to [itex] \lambda^2 + 1 [/itex])

of the log-likelihood function [itex] \ln{L(\lambda^2+1)}=-n\lambda + \Sigma_{i=1}^n x_i \ln{\lambda} - \ln{\Pi_{i=1}^n x_i!} [/itex]
 
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  • #2
mrkb80 said:
I found [itex]\hat{\lambda}=\bar{x}[/itex]

Can I just square it and add 1 and solve for lambda hat?
That's my understanding of how MLE works. If α is the value of λ that maximises the likelihood of the observed data, then (α2+1) must be the value of λ2+1 that does the same.
 
  • #3
cool. thanks again.
 

1. What is a MLE of Poisson Distribution?

A MLE (Maximum Likelihood Estimator) of Poisson distribution is a statistical method used to estimate the parameter λ of a Poisson distribution by finding the value that maximizes the likelihood function of the observed data.

2. How is the MLE of Poisson Distribution calculated?

The MLE of Poisson distribution is calculated by taking the derivative of the likelihood function with respect to λ, setting it equal to 0, and solving for λ. This value represents the maximum likelihood estimate for λ.

3. What is the significance of λ^2+1 in the MLE of Poisson Distribution?

In the MLE of Poisson distribution, λ^2+1 represents the parameter λ that is being estimated. This value is squared and added to 1 for mathematical convenience and to ensure that the resulting estimate is always positive.

4. Why is the MLE of Poisson Distribution important?

The MLE of Poisson distribution is important because it is a commonly used method for estimating the parameter λ in a Poisson distribution. This distribution is often used to model count data and the MLE provides a way to estimate the most likely value of λ based on the observed data.

5. Are there any limitations to using MLE of Poisson Distribution?

Yes, there are some limitations to using the MLE of Poisson distribution. It assumes that the data follows a Poisson distribution, which may not always be the case. It also requires a sufficiently large sample size for the estimate to be accurate. Additionally, the MLE may be biased if the assumptions are not met or if there are outliers in the data.

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