ML estimation

Let K be a Poisson random variable with parameter λ: its PMF is

pK(k;λ)=λke−λk!,for k=0,1,2,….

What is the ML estimate of λ based on a single observation K=k? (Your answer should be an algebraic function of k using standard notation.)
unanswered

Answer = k

The maximum likelihood (ML) estimate of λ based on a single observation K=k can be found by maximizing the likelihood function.

The likelihood function is defined as the probability of observing the given data, assuming a particular value for the parameter. In this case, it is the probability of observing K=k given the parameter λ.

The likelihood function L(λ|k) is given by:

L(λ|k) = pK(k;λ) = λ^k * e^(-λ) / k!

To find the ML estimate of λ, we need to find the value of λ that maximizes this likelihood function. This can be done by taking the derivative of the likelihood function with respect to λ and setting it equal to zero.

∂L(λ|k) / ∂λ = 0

Differentiating the likelihood function with respect to λ, we get:

∂L(λ|k) / ∂λ = kλ^(k-1) * e^(-λ) / k! - λ^k * e^(-λ) / k! = 0

Simplifying further, we can cancel out the common terms:

kλ^(k-1) - λ^k = 0

Factoring out λ^(k-1), we get:

λ^(k-1) * (k - λ) = 0

To satisfy this equation, either λ^(k-1) = 0 or (k - λ) = 0. Since λ cannot be zero, we have:

k - λ = 0

Solving for λ, we find:

λ = k

Therefore, the ML estimate of λ based on a single observation K=k is λ = k.

To find the maximum likelihood (ML) estimate of λ based on a single observation K=k, we need to maximize the likelihood function L(λ), which is the probability mass function (PMF) of the Poisson distribution.

The likelihood function L(λ) is given by:

L(λ) = pK(k; λ) = λ^k * e^(-λ) / k!

To find the maximum, we can take the derivative with respect to λ and set it equal to zero:

dL(λ)/dλ = 0

To simplify the calculation, let's take the natural logarithm of the likelihood function and find the maximum of the log-likelihood instead:

ln L(λ) = ln(λ^k * e^(-λ) / k!) = k * ln(λ) - λ - ln(k!)

Taking the derivative of ln L(λ) with respect to λ:

d(ln L(λ))/dλ = k/λ - 1

Setting the derivative equal to zero and solving for λ:

k/λ - 1 = 0
k = λ

Therefore, the ML estimate of λ based on a single observation K=k is λ = k.

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