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Calculate Bühlmann's k for aggregate losses. (A) Less than 1 (B) At least 1, but less than 2 (C) At least 2, but less than 3 (D) At least 3, but less than 4 (E) At least 4 5 LO

Calculate Bühlmann's k for aggregate losses. (A) Less than 1 (B) At least 1, but less than 2 (C) At least 2, but less than 3 (D) At least 3, but less than 4 (E) At least 4 5 LO

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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(C) 0.105 (D) 0.210 (E) 0.420 8. You are given: (i) Claim counts follow a Poisson distribution with mean 0 . (ii) Claim sizes follow an exponential distribution with mean 10. (iii) Claim counts and claim sizes are independent, given 0 . (iv) The prior distribution has probability density function: a(0) =- 5 0 >1 06 Calculate Bühlmann's k for aggregate losses. (A) Less than 1 (B) At least 1, but less than 2 (C) At least 2, but less than 3 (D) At least 3, but less than 4 (E) At least 4 5 9. You are given: A survival study uses a Cox proportional hazards model with covariates Z and Z each taking the value 0 or 1. (i) (ii) The maximum partial likelihood estimate of the coefficient vector is: (Bi, B2) = (0.71, 0.20) The baseline survival function at time to is estimated as S(t) = 0.65. (iii) Estimate S(to) for a subject with covariate values Z = Z2 = 1.
Transcribed Image Text:(C) 0.105 (D) 0.210 (E) 0.420 8. You are given: (i) Claim counts follow a Poisson distribution with mean 0 . (ii) Claim sizes follow an exponential distribution with mean 10. (iii) Claim counts and claim sizes are independent, given 0 . (iv) The prior distribution has probability density function: a(0) =- 5 0 >1 06 Calculate Bühlmann's k for aggregate losses. (A) Less than 1 (B) At least 1, but less than 2 (C) At least 2, but less than 3 (D) At least 3, but less than 4 (E) At least 4 5 9. You are given: A survival study uses a Cox proportional hazards model with covariates Z and Z each taking the value 0 or 1. (i) (ii) The maximum partial likelihood estimate of the coefficient vector is: (Bi, B2) = (0.71, 0.20) The baseline survival function at time to is estimated as S(t) = 0.65. (iii) Estimate S(to) for a subject with covariate values Z = Z2 = 1.
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