CAS Exam MAS-II Fall 2019, Question 26
Study guide: Exam MAS-II
You observe a linear relationship between the amount of snowfall in a territory and the number of collision claims, Y, filed by insured drivers in that territory. You describe that relationship as follows:
\begin{align*}
Y &\sim \text{Normal}(\mu, \sigma) \\
\mu &= \beta_0 + \beta_1 \ast snowfall
\end{align*}You decide to model this relationship using linear regression with Ordinary Least Squares (OLS) and compare it to the same model fit using Bayesian Markov chain Monte Carlo (MCMC), with appropriate priors.
You are given the following statements regarding your models:
I. The MCMC model is specified with one likelihood function and three prior distributions.
II. Parameter estimates from an OLS model and MCMC model will yield similar estimates when using flat priors.
III. The OLS regression provides information on the uncertainty of the estimate of the sigma parameter (\sigma).
Determine which of the above statements are true about your models.
- A) None of I, II, or II is true
- B) I and II only
- C) I and III only
- D) II and III only
- E) The answer is not given by (A), (B), (C), or (D)
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