[[Actuarial Notes Wiki|Wiki]] / **MAS-I (CAS)** ## MAS-I (CAS) The **Modern Actuarial Statistics I** is a 4-hour computer-based exam covering [[Stochastic Processes]], [[Survival Models]], [[Statistics]], and [[Generalized Linear Models]] as part of the ACAS credentialing pathway. <div class="callout-cols-2"> > [!answer]- 📅 Exam Schedule 2026 > > <div class="highlight-upcoming" data-date-col="0"></div> > > |Dates|Exam| > |---|---| > |Jan 28 - Feb 3|MAS-I| > |Apr 22 - May 1|MAS-I| > |Jul 29 - Aug 4|MAS-I| > |Oct 28 - Nov 5|MAS-I| > > - [Register](https://www.casact.org/exams-admissions/exam-registration) ($550 registration fee) > [!answer]- 📄 Download Resources 2 PDFs > > - [Content Outline (August 2025)](https://www.casact.org/sites/default/files/2025-08/MAS_I_Content_Outline__August_2025_.pdf) > - [CAS Exam MAS-I Page](https://www.casact.org/exam/exam-mas-i-modern-actuarial-statistics-i) </div> > [!answer]- 📕 Source Material 8 Sources > > |Source|Domains / Tasks| > |---|---| > |[[Poisson Processes and Mixture Distributions (Daniel - 2008)]]|A1–A5| > |[[An Introduction to Generalized Linear Models (Dobson - 2018)]]|C1–C9| > |[[Introduction to Mathematical Statistics (Hogg et al. - 2018)]]|B1–B8, C1–C9| > |[[An Introduction to Statistical Learning (James et al. - 2021)]]|C1–C9| > |[[Generalized Linear Models (Larsen - 2015)]]|C1–C9| > |[[Introduction to Probability Models (Ross - 2019)]]|A1–A6| > |[[Life Contingencies (Struppeck - 2014)]]|A5–A6| > |[[Nonlife Actuarial Models (Tse - 2009)]]|B1–B4, B7–B9| ### Learning Objectives > [!example]- A. Probability Models (Stochastic Processes and Survival Models) {20–30%} > > ### A. Probability Models (Stochastic Processes and Survival Models) > > Candidates should be able to solve problems using [[Stochastic Processes]] and determine the probabilities and distributions associated with these processes. > > 1. Model claim frequencies using [[Poisson Process]]es > 2. Calculate expected values, variances, and probabilities for any [[Poisson Process]] > 3. Calculate [[Limited Expected Value]] > 4. Perform [[Survival Model]] and [[Hazard Rate]] calculations > 5. Perform [[Joint Life]] calculations > 6. Calculate simple [[Whole Life Insurance]] or [[Life Annuity]] problems > > **Readings:** Daniel · Ross · Struppeck > [!example]- B. Statistics {20–30%} > > ### B. Statistics > > Candidates should be able to apply the concepts typically covered in the second semester of a two-semester undergraduate sequence in [[Probability]] and [[Statistics]]. > > 1. Estimate the mean and variance given a sample > 2. Estimate a [[Sufficient Statistic]] for a distribution > 3. Test statistical hypotheses, including [[Type I Error]] and [[Type II Error]] > 4. Test means and variances using critical values from a [[Sampling Distribution]] > 5. Model insurance claim frequency and severity > 6. Model insurance claims in aggregate using [[Aggregate Loss Model]]s > 7. Calculate [[Order Statistics]] of a sample > 8. Perform point estimation of statistical parameters using [[Maximum Likelihood Estimation]] (MLE) applying criteria such as consistency, [[Unbiasedness]], [[Sufficiency]], efficiency, [[Minimum Variance]], and [[Mean Square Error]] (e.g., accounting for censoring and truncation in the data) > 9. Adjust calculations for the effect of missing data values, including [[Censoring]] and [[Truncation]] > > **Readings:** Hogg, McKean, and Craig · Tse > [!example]- C. Extended Linear Models {45–55%} > > ### C. Extended Linear Models > > Candidates should be able to solve problems using extended linear models and determine when these models are appropriate to use. > > 10. Select the appropriate model for an extended linear model > 11. Select the appropriate model structure for an extended linear model given the behavior of the data set (e.g., appropriate [[Link Function]] and distribution for the dependent variable for [[Generalized Linear Model]]) > 12. Evaluate models developed using an extended linear model approach > 13. Interpret the extended linear model output from statistical software, such as parameter estimate tables and [[ANOVA]] tables > 14. Distinguish among categorical, ordinal, and continuous predictors and their interactions, and how these relate to their usage in an extended linear model > 15. Understand and apply control and offset variables in [[Generalized Linear Model]]s > 16. Understand and calculate [[AIC]], [[BIC]], [[Deviance]], and [[R-Squared]] > 17. Analyze model diagnostic plots (e.g., [[Residual Plot]]s, marginal model, [[QQ Plot]]s, and added variable plots) to assess quality of fit > 18. Interpret [[Exploratory Data Analysis]] plots for various data types (e.g., box plots, univariate plots, histograms) > > **Readings:** Dobson and Barnett · Hogg, McKean, and Craig · James et al. · Larsen