Yaning Zhang
Title: Dimension Reduction Methods with Application in Automobile Insurance
Date: January 26th, 2023
Time: 3:00pm
Location: Hybrid, over zoom and in LIB 2020
Abstract
Motivated by data explosion in the automobile industry due to technological innovations, this report aims to provide an outline of how dimension reduction methods can be used in modeling automobile insurance claim amounts. The framework is based on a generalized linear model (GLM) with a Tweedie distribution. Three popular methods are discussed in detail, the stepwise method, the principal component analysis (PCA) using the nonlinear iterative partial least squares (NIPALS) method, and the partial least squares method. The effectiveness and predictability of three methods are compared using a car insurance data example. The results show that a small number of latent variables can effectively capture sufficient information in the explanatory variables, and can be utilized to build a decent predictive model for loss costs. Our study confirms that when multicollinearity exists in the dataset, using orthogonal latent variables can generally result in better modeling performance than ordinary variable selection methods.