3345 actuarial statistics module 3 semi parametric

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Unformatted text preview: 0.1386 · 0.068315 0.126797 0.068212 · 0.6383 0.068965 0.068212 0.178264 1.6931 = 0.7586652 32/45 The p -value is Pr(χ2 ≥ 0.7586652) = 0.859(> 0.05). 3 There is no statistical evidence to reject H0 . Actuarial Statistics – Module 3: Semi-parametric methods: Cox Regression Model Hypothesis tests on the β ’s Interaction Sometimes, the effects of some covariates depend on the presence or absence of each other. In this case, there are interactions between these covariates. To test for interaction we need to test the effect of the combination of the covariates. For example, we want to test for interaction between smoking status and high blood pressure. If Z2 represents the smoking status and Z3 represents blood pressure. We need to test the effect of an extra covariate Z3 = Z2 × Z3 , which represents the combination of Z2 and Z3 . Interaction is not required study in this subject. 33/45 Actuarial Statistics – Module 3: Semi-parametric methods: Cox Regression Model Estimation of the full survival function 1 Introduction 2 Main assumptions 3 On the proportionality of hazard rates 4 Estimation of the regression parameters β 5 Hypothesis tests on the β ’s 6 Estimation of the full survival function 7 Diagnostics for the Cox regression model 33/45 Actuarial Statistics – Module 3: Semi-parametric methods:...
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This document was uploaded on 04/03/2014.

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