Role of Generalised Linear Model in non-life pricing Phase3

Image
Before reading this article, make sure that you read phase1 and phase2. Here are the link:
Phase1: http://www.actuarysense.com/2018/10/role-of-generalised-linear-model-in-non.html
Phase2: http://www.actuarysense.com/2018/11/role-of-generalised-linear-model-in-non.html So we know that the purpose of GLM is to find the relationship between mean of the response variable and covariates.

In this Article we are going to talk about Linear Predictors.
Linear Predictor: Let’s denote it with, “η” (eta). So, linear predictor is actually a function of covariates. For example, in the normal linear model where function is Y = B0 + B1x. So linear predictor will be η = B0 + B1x. Always note that linear predictor has to be linear in its parameter. In this case parameters are B0 and B1. But still the question is how I came up with B0 + B1x as a function? First of all, note that broadly there are two types of Covariates. 1. Variables: It takes the numerical value. For example: age of policyholder, years of ex…

Difference between Estimate and Estimator ?

Estimator:

It is a Random Variable. So its value depends on the outcome of some experiment, and it has a statistical distribution.

Estimate:

It's value is Constant. It is the value taken by an estimator, given a particular set of sample data.


Note:
  • One ESTIMATE will never give you multiple ESTIMATORS but one ESTIMATOR will give you multiple ESTIMATES.
  • ESTIMATOR can be a good or bad, but ESTIMATE can never be good or bad.

Comments

Popular posts from this blog

Term structure of interest Rates : Theories Explained

NPV vs IRR : Which is better and Why

Pension Plans: DB vs DC