Exam SRM – Statistics for Risk Modeling

5 个单元 · 28 个章节

免费 Exam SRM – Statistics for Risk Modeling 学习笔记，适合 SOA (Society of Actuaries) 学生。每个章节都覆盖一个重点主题，并附例题与可延伸到 thinka app 的练习提示。

Basics of Statistical Learning

Supervised vs. unsupervised and regression vs. classification
Assessing model accuracy
The bias-variance tradeoff
Training set vs. test set validation
k-fold cross-validation
Leave-one-out cross-validation

Linear Models

Ordinary least squares vs. generalized linear model assumptions
The exponential family and link functions
Interpreting parameters in a business context
Diagnostic tests of model fit and assumption checking
Model selection criteria: t and F tests, AIC, BIC, and likelihood ratio test
Predicted values, confidence intervals, and prediction intervals
Regularized regression and K-nearest neighbors

Time Series Models

Stochastic time series: random walks, stationarity, and autocorrelation
Exponential smoothing models
Autoregressive models
Autoregressive conditionally heteroskedastic models
Predicted values and confidence intervals

Decision Trees

Constructing and pruning decision trees
Classification trees
Regression trees
Bagging, boosting, and random forests
Comparing decision trees to linear models

Unsupervised Learning Techniques

Principal components and how they are calculated
Interpreting principal components analysis
K-means clustering
Hierarchical clustering
Deciding the number of clusters

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