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: 7(0) = 0>1 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

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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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