Exam MAS-I: Modern Actuarial Statistics I Study Guide
Probability, statistics, regression, time series, Bayesian analysis, and simulation for CAS candidates.
Overview
Exam MAS-I (Modern Actuarial Statistics I) covers probability, mathematical statistics, regression, time series, and Bayesian methods for CAS candidates. It emphasizes both theoretical understanding and the ability to apply statistical methods to insurance data. The exam replaced the former Exam S and Exam 3L statistical content.
Exam Format
- Duration
- 240 minutes (4 hours)
- Questions
- 40 multiple-choice questions
- Pathway
- CAS
- Format
- Computer-based testing (CBT)
Topic Breakdown
Probability
15-20%Distributions, moment-generating functions, transformations, order statistics, multivariate distributions
Mathematical Statistics
20-25%Point estimation (MLE, method of moments), confidence intervals, hypothesis testing, sufficiency
Regression
15-20%Multiple linear regression, diagnostics, variable selection, weighted least squares, GLMs
Time Series
10-15%Stationary processes, AR, MA, ARMA, ARIMA models, forecasting, spectral analysis
Bayesian Analysis
15-20%Prior and posterior distributions, conjugate priors, credible intervals, predictive distributions
Simulation
5-10%Inverse transform, acceptance-rejection, variance reduction (antithetic, control variates, stratified sampling)
Recommended Study Approach
- 1
Review core probability distributions and their properties, including moment-generating functions and transformations.
- 2
Master maximum likelihood estimation, including setting up likelihood functions and solving score equations.
- 3
Study hypothesis testing with a focus on likelihood ratio tests, chi-squared tests, and p-value interpretation.
- 4
Practice regression problems involving diagnostics, influential observations, and model comparison.
- 5
For Bayesian analysis, work through conjugate prior problems (Beta-Binomial, Gamma-Poisson, Normal-Normal) repeatedly.
- 6
Learn simulation techniques including inverse transform, acceptance-rejection, and variance reduction methods.