MLE for Poisson distribution Calculate likelihood function $$L(\lambda) = \prod_{i=1}^m P(x_i | \lambda)$$ $$L(\lambda) = \prod_{i=1}^m \frac{e^{-\lambda} \lambda^{x_i}}{x_i!}$$
Added by Nicol-S H.
Close
Step 1
Step 1: The likelihood function for a Poisson distribution is given by: $$L(\lambda) = \prod_{i=1}^m P(x_i | \lambda)$$ where $x_i$ is the observed value of the $i$th observation and $\lambda$ is the parameter of the Poisson distribution. Show more…
Show all steps
Your feedback will help us improve your experience
Sri K and 57 other Intro Stats / AP Statistics educators are ready to help you.
Ask a new question
Labs
Want to see this concept in action?
Explore this concept interactively to see how it behaves as you change inputs.
Key Concepts
Recommended Videos
Use the maximum likelihood estimation (MLE) method to develop a formula that estimates the parameter of the Poisson distribution whose equation is p(x | ) = (e^(-) * ^x) / x!
Sri K.
1) Let Y1, ..., Yn be a random sample from the Poisson distribution with mean λ. a) Find the maximum likelihood estimator λ̂ for λ. b) Find the expected value and variance of λ̂. d) What is the MLE for P(Y = 0) = e^-λ?
Adi S.
Let $X_{1}, X_{2}, \ldots, X_{n}$ be a random sample from a Poisson distribution with parameter $\theta>0$ (a) Find the MVUE of $P(X \leq 1)=(1+\theta) e^{-\theta}$. Hint: $\quad$ Let $u\left(x_{1}\right)=1, x_{1} \leq 1$, zero elsewhere, and find $E\left[u\left(X_{1}\right) \mid Y=y\right]$, where $Y=\sum_{1}^{n} X_{i}$ (b) Express the MVUE as a function of the mle of $\theta$. (c) Determine the asymptotic distribution of the mle of $\theta$. (d) Obtain the mle of $P(X \leq 1)$. Then use Theorem $5.2 .9$ to determine its asymptotic distribution.
Sufficiency
Functions of a Parameter
Recommended Textbooks
Elementary Statistics a Step by Step Approach
The Practice of Statistics for AP
Introductory Statistics
Transcript
18,000,000+
Students on Numerade
Trusted by students at 8,000+ universities
Watch the video solution with this free unlock.
EMAIL
PASSWORD