**BEGINNING OF EXAMINATION** The annual number of claims for an insured has probability function: , 0 < q < 1.


 Wilfred Gibson
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1 **BEGINNING OF EXAMINATION** 1. You are given: (i) The annual number of claims for an insured has probability function: 3 p x q q x x ( ) = ( 1 ) 3 x, x = 0,1,, 3 (ii) The prior density is π ( q) = q, 0 < q < 1. A randomly chosen insured has zero claims in Year 1. Using Bühlmann credibility, estimate the number of claims in Year for the selected insured. (A) 0.33 (B) 0.50 (C) 1.00 (D) 1.33 (E) 1.50 Course 4: Fall GO ON TO NEXT PAGE
2 . You are given the following random sample of 13 claim amounts: Determine the smoothed empirical estimate of the 35 th percentile. (A) 19.4 (B) 31.3 (C) 34.7 (D) 46.6 (E) 56.8 Course 4: Fall GO ON TO NEXT PAGE
3 3. You are given: (i) (ii) (iii) Y is the annual number of discharges from a hospital. X is the number of beds in the hospital. Dummy D is 1 if the hospital is private and 0 if the hospital is public. (iv) The proposed model for the data is Y = β + β X + β D+ ε 1 3. (v) To correct for heteroscedasticity, the model Y / X = β / X + β + β D/ X + ε / X fitted to N = 393 observations, yielding ˆ β = 3.1, ˆ β 1 =.8 and β 3 = is (vi) For the fit in (v) above, the matrix of estimated variances and covariances of β, β 1 and β 3 is: Determine the upper limit of the symmetric 95% confidence interval for the difference between the mean annual number of discharges from private hospitals with 500 beds and the mean annual number of discharges from public hospitals with 500 beds. (A) 6 (B) 31 (C) 37 (D) 40 (E) 67 Course 4: Fall GO ON TO NEXT PAGE
4 4. For observation i of a survival study: d i is the left truncation point x i is the observed value if not right censored u i is the observed value if right censored You are given: Observation (i) d i x i u i Determine the KaplanMeier ProductLimit estimate, S 10 (1.6). (A) Less than 0.55 (B) At least 0.55, but less than 0.60 (C) At least 0.60, but less than 0.65 (D) At least 0.65, but less than 0.70 (E) At least 0.70 Course 4: Fall GO ON TO NEXT PAGE
5 5. You are given: (i) Two classes of policyholders have the following severity distributions: Claim Amount Probability of Claim Amount for Class 1 Probability of Claim Amount for Class , , (ii) Class 1 has twice as many claims as Class. A claim of 50 is observed. Determine the Bayesian estimate of the expected value of a second claim from the same policyholder. (A) Less than 10,00 (B) At least 10,00, but less than 10,400 (C) At least 10,400, but less than 10,600 (D) At least 10,600, but less than 10,800 (E) At least 10,800 Course 4: Fall GO ON TO NEXT PAGE
6 6. You are given the following three observations: You fit a distribution with the following density function to the data: b g b g p f x = p+ 1 x, 0< x< 1, p > 1 Determine the maximum likelihood estimate of p. (A) 4.0 (B) 4.1 (C) 4. (D) 4.3 (E) 4.4 Course 4: Fall GO ON TO NEXT PAGE
7 7. Which of the following statements about time series is false? (A) (B) (C) (D) (E) If the characteristics of the underlying stochastic process change over time, then the process is nonstationary. A stationary process is one whose joint distribution and conditional distribution functions are both invariant with respect to displacement in time. If a time series is stationary, then its mean, variance and, for any lag k, covariance must also be stationary. If the autocorrelation function for a time series is zero (or close to zero) for all lags k > 0, then no model can provide useful minimum meansquareerror forecasts of future values other than the mean. A homogeneous nonstationary time series will always be nonstationary, regardless of how many times it is differenced. Course 4: Fall GO ON TO NEXT PAGE
8 8. You are given the following sample of claim counts: You fit a binomial(m, q) model with the following requirements: (i) (ii) The mean of the fitted model equals the sample mean. The 33 rd percentile of the fitted model equals the smoothed empirical 33 rd percentile of the sample. Determine the smallest estimate of m that satisfies these requirements. (A) (B) 3 (C) 4 (D) 5 (E) 6 Course 4: Fall GO ON TO NEXT PAGE
9 9. Members of three classes of insureds can have 0, 1 or claims, with the following probabilities: Number of Claims Class 0 1 I II III A class is chosen at random, and varying numbers of insureds from that class are observed over years, as shown below: Year Number of Insureds Number of Claims Determine the BühlmannStraub credibility estimate of the number of claims in Year 3 for 35 insureds from the same class. (A) 10.6 (B) 10.9 (C) 11.1 (D) 11.4 (E) 11.6 Course 4: Fall GO ON TO NEXT PAGE
10 10. You are given the following random sample of 30 auto claims: ,100 1,500 1,800 1,90,000,450,500,580,910 3,800 3,800 3,810 3,870 4,000 4,800 7,00 7,390 11,750 1,000 15,000 5,000 30,000 3,300 35,000 55,000 You test the hypothesis that auto claims follow a continuous distribution F(x) with the following percentiles: x ,498 4,876 7,498 1,930 F(x) You group the data using the largest number of groups such that the expected number of claims in each group is at least 5. Calculate the chisquare goodnessoffit statistic. (A) Less than 7 (B) At least 7, but less than 10 (C) At least 10, but less than 13 (D) At least 13, but less than 16 (E) At least 16 Course 4: Fall GO ON TO NEXT PAGE
11 11. For the model Y = β1+ βx + β3x3+ ε, you are given: (i) (ii) r = 0.4 YX r i = 0.4 YX3 X Determine R. (A) 0.03 (B) 0.16 (C) 0.9 (D) 0.71 (E) 0.84 Course 4: Fall GO ON TO NEXT PAGE
12 1. The interval (0.357, 0.700) is a 95% logtransformed confidence interval for the cumulative hazard rate function at time t, where the cumulative hazard rate function is estimated using the NelsonAalen estimator. Determine the value of the NelsonAalen estimate of S(t). (A) 0.50 (B) 0.53 (C) 0.56 (D) 0.59 (E) 0.61 Course 4: Fall GO ON TO NEXT PAGE
13 13. You are given: (i) (ii) The number of claims observed in a 1year period has a Poisson distribution with mean θ. The prior density is: θ e π( θ) =, 0< θ < k k 1 e (iii) The unconditional probability of observing zero claims in 1 year is Determine k. (A) 1.5 (B) 1.7 (C) 1.9 (D).1 (E).3 Course 4: Fall GO ON TO NEXT PAGE
14 14. The parameters of the inverse Pareto distribution τ F( x) = x/( x+ θ ) are to be estimated using the method of moments based on the following data: Estimate θ by matching kth moments with k = 1 and k =. (A) Less than 1 (B) At least 1, but less than 5 (C) At least 5, but less than 5 (D) At least 5, but less than 50 (E) At least 50 Course 4: Fall GO ON TO NEXT PAGE
15 15. You are given the following information about a stationary timeseries model: ρ = ρ = You are also given that θ + θ =.. ρ = 0, k = 3,4,5,... k 1 07 Determine θ 1. (A) 0.1 (B) 0. (C) 0.3 (D) 0.4 (E) 0.5 Course 4: Fall GO ON TO NEXT PAGE
16 16. A sample of claim amounts is {300, 600, 1500}. By applying the deductible to this sample, the loss elimination ratio for a deductible of 100 per claim is estimated to be You are given the following simulations from the sample: Simulation Claim Amounts Determine the bootstrap approximation to the mean square error of the estimate. (A) (B) (C) 0.01 (D) (E) Course 4: Fall GO ON TO NEXT PAGE
17 17. You are given the following commercial automobile policy experience: Losses Number of Automobiles Losses Number of Automobiles Losses Number of Automobiles Company Year 1 Year Year 3 I 50,000 50,000? ? II? 150, ,000? III 150,000? 150,000 50? 150 Determine the nonparametric empirical Bayes credibility factor, Z, for Company III. (A) Less than 0. (B) At least 0., but less than 0.4 (C) At least 0.4, but less than 0.6 (D) At least 0.6, but less than 0.8 (E) At least 0.8 Course 4: Fall GO ON TO NEXT PAGE
18 18. Let x1, x,..., x n and y1, y,..., y m denote independent random samples of losses from Region 1 and Region, respectively. Singleparameter Pareto distributions with θ = 1, but different values of α, are used to model losses in these regions. Past experience indicates that the expected value of losses in Region is 1.5 times the expected value of losses in Region 1. You intend to calculate the maximum likelihood estimate of α for Region 1, using the data from both regions. Which of the following equations must be solved? n ln i 0 α = (A) ( x ) (B) ( x ) ( α + ) ln( yi ) 3 ( + ) n m ln i + = 0 α α α (C) ( x ) ( yi ) ( ) n m ln ln i + = 0 α 3α( α + ) α + (D) ( x ) ( yi ) ( ) n m 6 ln ln i + = 0 α α( α + ) α + (E) ( x ) ( yi ) ( ) n 3m 6 ln ln i + = 0 α α( 3 α) 3 α Course 4: Fall GO ON TO NEXT PAGE
19 19. You are given the following information about a linear regression model: (i) The unit of measurement is a region, and the number of regions in the study is 37. (ii) (iii) The dependent variable is a measure of workers compensation frequency, while the three independent variables are a measure of employment, a measure of unemployment rate and a dummy variable indicating the presence or absence of vigorous costcontainment efforts. The model is fitted separately to the group of 18 largest regions and to the group of 19 smallest regions (by population). The ESS resulting from the first fit is 4053, while the ESS resulting from the second fit is 087. (iv) The model is fitted to all 37 regions, and the resulting ESS is 10,374. The null hypothesis to be tested is that the pooling of the regions into one group is appropriate. Which of the following is true? (A) (B) (C) (D) (E) The F statistic has 4 numerator degrees of freedom and 9 denominator degrees of freedom, and it is statistically significant at the 5% significance level. The F statistic has 4 numerator degrees of freedom and 9 denominator degrees of freedom, and it is not statistically significant at the 5% significance level. The F statistic has 4 numerator degrees of freedom and 33 denominator degrees of freedom, and it is not statistically significant at the 5% significance level. The F statistic has 8 numerator degrees of freedom and 33 denominator degrees of freedom, and it is statistically significant at the 5% significance level. The F statistic has 8 numerator degrees of freedom and 33 denominator degrees of freedom, and it is not statistically significant at the 5% significance level. Course 4: Fall GO ON TO NEXT PAGE
20 0. From a population having distribution function F, you are given the following sample:.0, 3.3, 3.3, 4.0, 4.0, 4.7, 4.7, 4.7 Calculate the kernel density estimate of F(4), using the uniform kernel with bandwidth 1.4. (A) 0.31 (B) 0.41 (C) 0.50 (D) 0.53 (E) 0.63 Course 4: Fall GO ON TO NEXT PAGE
21 1. You are given: (i) The number of claims has probability function: m p x q q x x ( ) = ( 1 ) m x, x = 0, 1,,, m (ii) The actual number of claims must be within 1% of the expected number of claims with probability (iii) The expected number of claims for full credibility is 34,574. Determine q. (A) 0.05 (B) 0.10 (C) 0.0 (D) 0.40 (E) 0.80 Course 4: Fall GO ON TO NEXT PAGE
22 . If the proposed model is appropriate, which of the following tends to zero as the sample size goes to infinity? (A) (B) (C) (D) (E) KolmogorovSmirnov test statistic AndersonDarling test statistic Chisquare goodnessoffit test statistic Schwarz Bayesian adjustment None of (A), (B), (C) or (D) Course 4: Fall GO ON TO NEXT PAGE
23 3. The model Y = β X + ε is fitted to the following observations: i i i X Y You are given: ( ε ) = σ Var i X i Determine the weighted leastsquares estimate of β. (A) Less than.5 (B) At least.5, but less than.8 (C) At least.8, but less than 3.1 (D) At least 3.1, but less than 3.4 (E) At least 3.4 Course 4: Fall GO ON TO NEXT PAGE
24 4. You are given: (i) Losses are uniformly distributed on ( 0,θ ) with θ > 150. (ii) The policy limit is 150. (iii) A sample of payments is: 14, 33, 7, 94, 10, 135, 150, 150 Estimate θ by matching the average sample payment to the expected payment per loss. (A) 19 (B) 196 (C) 00 (D) 04 (E) 08 Course 4: Fall GO ON TO NEXT PAGE
25 5. You are given: (i) (ii) (iii) (iv) (v) A portfolio of independent risks is divided into two classes. Each class contains the same number of risks. For each risk in Class 1, the number of claims per year follows a Poisson distribution with mean 5. For each risk in Class, the number of claims per year follows a binomial distribution with m = 8 and q = A randomly selected risk has three claims in Year 1, r claims in Year and four claims in Year 3. The Bühlmann credibility estimate for the number of claims in Year 4 for this risk is Determine r. (A) 1 (B) (C) 3 (D) 4 (E) 5 Course 4: Fall GO ON TO NEXT PAGE
26 6. You are given: (i) A sample of losses is: (ii) No information is available about losses of 500 or less. (iii) Losses are assumed to follow an exponential distribution with mean θ. Determine the maximum likelihood estimate of θ. (A) 33 (B) 400 (C) 500 (D) 733 (E) 133 Course 4: Fall GO ON TO NEXT PAGE
27 7. You are given the following model: d i d i lnyt = β1+ βln Xt + β3ln X3t + β4 ln Xt ln Xt Dt + β ln X t ln X t Dt + ε t where, t indexes the years and t 0 is Y is a measure of workers compensation frequency. X is a measure of employment level. X 3 is a measure of unemployment rate. D t is 0 for t t 0 and 1 for t > t 0. Fitting the model yields: β ˆ = Estimate the elasticity of frequency with respect to employment level for 199. (A) 0.11 (B) 0.53 (C) 0.60 (D) 0.90 (E) 1.70 Course 4: Fall GO ON TO NEXT PAGE
28 8. You use a Cox proportional hazards model with a datadependent model for the baseline hazard rate to represent the mortality of two groups of two lives each. The lives in Group A die at times t 1 and t 4, and the lives in Group B die at times t and t 3, wheret 1 < t < t 3 < t 4. Which of the following statements is true for t > t 1? (A) (B) (C) (D) (E) The estimated cumulative hazard rate function for Group A is everywhere smaller than the estimated cumulative hazard rate function for Group B. The estimated cumulative hazard rate function for Group A is everywhere larger than the estimated cumulative hazard rate function for Group B. The estimated cumulative hazard rate function for Group A is identical to the estimated cumulative hazard rate function for Group B. The estimated cumulative hazard rate function for Group A is neither everywhere smaller nor everywhere larger nor identical to the estimated cumulative hazard rate function for Group B. The relationship between the estimated cumulative hazard rate function for Group A and the estimated cumulative hazard rate function for Group B cannot be determined without knowing the times at which the deaths occurred. Course 4: Fall GO ON TO NEXT PAGE
29 9. You are given: (i) Claim counts follow a Poisson distribution with mean λ. (ii) Claim sizes follow a lognormal distribution with parameters µ and σ. (iii) (iv) Claim counts and claim sizes are independent. The prior distribution has joint probability density function: ( ) f λ, µσ, = σ, 0< λ < 1, 0 < µ < 1, 0< σ < 1 Calculate Bühlmann s k for aggregate losses. (A) Less than (B) At least, but less than 4 (C) At least 4, but less than 6 (D) At least 6, but less than 8 (E) At least 8 Course 4: Fall GO ON TO NEXT PAGE
30 30. You are given the following data: You use the method of percentile matching at the 40 th and 80 th percentiles to fit an Inverse Weibull distribution to these data. Determine the estimate of θ. (A) Less than 1.35 (B) At least 1.35, but less than 1.45 (C) At least 1.45, but less than 1.55 (D) At least 1.55, but less than 1.65 (E) At least 1.65 Course 4: Fall GO ON TO NEXT PAGE
31 31. An ARMA(p, q) model is used to represent a time series. You perform diagnostic checking to test whether the model is specified correctly. Which of the following is false? (A) If the model is correctly specified, then for large displacements k, the residual autocorrelations rˆ k are themselves uncorrelated, normally distributed random variables with mean 0 and variance 1/T, where T is the number of observations in the time series. (B) The BoxPierce Q statistic, where Q = T rˆ k, is approximately distributed as chisquare with K degrees of freedom. K k = 1 (C) (D) (E) For loworder models, K equal to 15 or 0 is sufficient for the BoxPierce Q statistic to be meaningful. The BoxPierce Q statistic is approximately chisquare distributed because the first few autocorrelations will have a variance slightly less than 1/T and may themselves be correlated. If the BoxPierce Q statistic is smaller than the 90 th percentile of the appropriate chisquare distribution, then the model is not rejected at the 10% significance level. Course 4: Fall GO ON TO NEXT PAGE
32 3. You are given: (i) The number of claims follows a Poisson distribution with mean λ. (ii) Observations other than 0 and 1 have been deleted from the data. (iii) The data contain an equal number of observations of 0 and 1. Determine the maximum likelihood estimate of λ. (A) 0.50 (B) 0.75 (C) 1.00 (D) 1.5 (E) 1.50 Course 4: Fall GO ON TO NEXT PAGE
33 33. You are given: (i) In a portfolio of risks, each policyholder can have at most one claim per year. (ii) The probability of a claim for a policyholder during a year is q. 3 q (iii) The prior density is π ( q ) =, 0.6 < q < A randomly selected policyholder has one claim in Year 1 and zero claims in Year. For this policyholder, determine the posterior probability that 0.7 < q < 0.8. (A) Less than 0.3 (B) At least 0.3, but less than 0.4 (C) At least 0.4, but less than 0.5 (D) At least 0.5, but less than 0.6 (E) At least 0.6 Course 4: Fall GO ON TO NEXT PAGE
34 34. You are given: (i) The ages and number of accidents for five insureds are as follows: Insured X = Age Y = Number of Accidents Total (ii) Y1, Y,..., Y 5 are independently Poisson distributed with means µ = β X, i = 1,,...,5. i i Estimate the standard deviation of ˆ β. (A) Less than (B) At least 0.015, but less than 0.00 (C) At least 0.00, but less than 0.05 (D) At least 0.05, but less than (E) At least Course 4: Fall GO ON TO NEXT PAGE
35 35. Which of the following statements regarding the ordinary least squares fit of the model Y = α + β X + ε is false? (A) (B) (C) (D) The lower the ratio of the standard error of the regression s to the mean of Y, the more closely the data fit the regression line. The precision of the slope estimator decreases as the variation of the X s increases. The residual variance s is an unbiased as well as consistent estimator of the error variance σ. If the mean of X is positive, then an overestimate of α is likely to be associated with an underestimate of β. (E) β is an unbiased estimator of β. Course 4: Fall GO ON TO NEXT PAGE
36 36. You are given: (i) The following is a sample of 15 losses: 11,,,, 36, 51, 69, 69, 69, 9, 9, 10, 161, 161, 30 (ii) H ˆ 1( x) is the NelsonAalen empirical estimate of the cumulative hazard rate function. (iii) H ˆ ( x ) is the maximum likelihood estimate of the cumulative hazard rate function under the assumption that the sample is drawn from an exponential distribution. Calculate Hˆ ˆ (75) H1(75). (A) 0.00 (B) 0.11 (C) 0. (D) 0.33 (E) 0.44 Course 4: Fall GO ON TO NEXT PAGE
37 37. For a portfolio of motorcycle insurance policyholders, you are given: (i) (ii) The number of claims for each policyholder has a conditional Poisson distribution. For Year 1, the following data are observed: Number of Claims Number of Policyholders Total 3000 Determine the credibility factor, Z, for Year. (A) Less than 0.30 (B) At least 0.30, but less than 0.35 (C) At least 0.35, but less than 0.40 (D) At least 0.40, but less than 0.45 (E) At least 0.45 Course 4: Fall GO ON TO NEXT PAGE
38 38. You are given a random sample of observations: You test the hypothesis that the probability density function is: 4 f x =, x> 0 ( ) ( 1+ x) 5 Calculate the KolmogorovSmirnov test statistic. (A) Less than 0.05 (B) At least 0.05, but less than 0.15 (C) At least 0.15, but less than 0.5 (D) At least 0.5, but less than 0.35 (E) At least 0.35 Course 4: Fall GO ON TO NEXT PAGE
39 39. You are given the following timeseries model: You are also given: y = 09. y + 1+ ε 04. ε t t 1 t t 1 y T T = 80. ε = 05. Calculate the twoperiod forecast, y T bg. (A) 8.0 (B) 8. (C) 8.4 (D) 8.6 (E) 8.8 Course 4: Fall GO ON TO NEXT PAGE
40 40. Which of the following statements is true? (A) (B) (C) (D) (E) A uniformly minimum variance unbiased estimator is an estimator such that no other estimator has a smaller variance. An estimator is consistent whenever the variance of the estimator approaches zero as the sample size increases to infinity. A consistent estimator is also unbiased. For an unbiased estimator, the mean squared error is always equal to the variance. One computational advantage of using mean squared error is that it is not a function of the true value of the parameter. **END OF EXAMINATION** Course 4: Fall STOP
41 Course 4, Fall 004 PRELIMINARY ANSWER KEY Question # Answer Question # Answer 1 C 1 B D A 3 D 3 A 4 E 4 E 5 B 5 C 6 D 6 A 7 E 7 B 8 E 8 A 9 C 9 E 10 A 30 D 11 C 31 B 1 E 3 C 13 C 33 D 14 C 34 B 15 E 35 B 16 A 36 B 17 B 37 A 18 D 38 E 19 A 39 B 0 D 40 D Course 4: Fall STOP
42 Fall 004 Course 4 Solutions Question #1 Key: C EX ( q) = 3 qvarx, ( q) = 3 q(1 q) µ = E (3 q ) = 3 q qdq = q = v= E [3 q (1 q )] = 3 q (1 q ) qdq= q 1.5 q = µ 0 0 a = Var (3 q ) = E (9 q ) = 9 q qdq = 4.5 q 4 = = 0.5 k = v/a = 0.5/0.5 = 1 1 Z = = The estimate is 0.5(0) + 0.5() = 1. Question # Key: D 0.35(14) = 4.9 ˆ π = 0.1(16) + 0.9(50) = Question #3 Key: D The problem asks for the confidence interval for β 3, which is 8 ± 1.96(38.843) 1/ or 8 ± 1 or [16, 40]. Question #4 Key: E At y 1 = 0.9 the risk set is r 1 = 7 and s 1 = 1. At y = 1.5 the risk set is r = 6 and s = Then, S 10(1.6) = =
43 Question #5 Key: B Pr( claim = 50 class1) Pr( class1) Pr ( class1 claim = 50) = Pr( claim = 50 class1) Pr( class1) + Pr( claim = 50 class) Pr( class) 0.5( / 3) 10 = =. 0.5( / 3) + 0.7(1/ 3) 17 Eclaim ( class 1) = 0.5(50) + 0.3(,500) + 0.(60, 000) = 1,875. Eclaim ( class ) = 0.7(50) + 0.(,500) + 0.1(60, 000) = 6, Eclaim ( 50) = (1,875) + (6, 675) = 10, Question #6 Key: D L( p) = f(0.74) f(0.81) f(0.95) p p p = ( p+ 1)0.74 ( p+ 1)0.81 ( p+ 1) p = ( p + 1) ( ) l( p) = ln L( p) = 3ln( p+ 1) + pln( ) 3 l'( p) = = 0 p p + 1 = = p = Question #7 Key: E Homogeneous nonstationary processes have the desirable property that if they are differenced one or more times, eventually one of the resulting series will be stationary. Question #8 Key: E The sample mean is 1 and therefore mq = 1. For the smoothed empirical 33 rd percentile, (1/3)(5 + 1) = and the second smallest sample item is 0. For the 33 rd percentile of the binomial distribution to be 0, the probability at zero must m 1 m exceed So, (1 q) > 0.33 and then (1 m ) > Trial and error gives m = 6 as the smallest value that produces this result.
44 Question #9 Key: C Let X be the number of claims. EX ( I) = 0.9(0) + 0.1() = 0. EX ( II) = 0.8(0) + 0.1(1) + 0.1() = 0.3 E( X III) = 0.7(0) + 0.(1) + 0.1() = 0.4 Var( X I) = 0.9(0) + 0.1(4) 0. = 0.36 Var( X II) = 0.8(0) + 0.1(1) + 0.1(4) 0.3 = 0.41 Var( X III) = 0.7(0) + 0.(1) + 0.1(4) 0.4 = µ = (1/ )( ) = 0.3 v = (1/ 3)( ) = a = (1/ 3)( ) 0.3 = k = / = Z = = For one insured the estimate is (17/50) (0.3) = For 35 insureds the estimate is 35( ) = Question #10 Key: A For the given intervals, based on the model probabilities, the expected counts are 4.8, 3.3, 8.4, 7.8,.7, 1.5, and 1.5. To get the totals above 5, group the first two intervals and the last three. The table is Interval Observed Expected Chisquare infinity Total
45 Question #11 Key: C r YX 3i X ( 0.4) = R R = 1 r YX ryx R (0.4) 1 (0.4) = 0.16(0.84) = Question #1 Key: E Let H ˆ = H ˆ() t and vˆ= Var ˆ ( Hˆ ( t)). The confidence interval is U = exp( ± z ˆ ˆ α / v/ H). Multiplying the two bounds gives ˆ 0.7(0.357) = H for H ˆ = Then, S ˆ = exp( ) = ĤU where Question #13 Key: C = Pr( N = 0) = Pr( N = 0 θπθ ) ( ) dθ e k 0 θ θ k k k θ e e e 1 = e dθ = = e (1 e ) (1 e ) (1 e ) k 1 e 1+ e = = k (1 e ) = (0.575) 1 = 0.15 k = k k k k k 0 k
46 Question #14 Key: C The sample 1 moment is = moment is = Then the equations are =, θτ ( 1) =. θ ( τ 1)( τ ) Divide the square of the first equation by the second equation to obtain τ = which is solved for τ = From the first equation, ( τ 1) 1 θ = = ( ) The sample  Question #15 Key: E θ This is an MA() model. For this model, ρ =. Substituting the given value of ρ 1 + θ1 + θ and θ = 0.7 θ1 gives (0.7 θ1) θ = = 1 + θ1 + (0.7 θ1 ) 1 + θ θ1+ θ ( θ1+ θ1 ) = θ θ1 0.31θ1 = θ1 0 = 0.31θ θ θ = 0.5 or 3. 1 Only the first solution (0.5) is acceptable.
47 Question #16 Key: A For each simulation, estimate the LER and then calculate the squared difference from the estimate, Simulation First claim Second claim Third claim LER Squared difference The last column has an average of which is the bootstrap estimate. Question #17 Key: B The subscripts denote the three companies. 50, , , 000 xi1 = = 500, xi = = 50, xii1 = = , , , 000 xii = = 500, xiii1 = = 3, 000, xiii = = 1, , , , , 000 xi = = , xii = = 375, xiii = = 1,500, x = = , ( ) + 00( ) + 500( ) + 300( ) + 50(3, 000 1,500) + 150(1, 000 1,500) vˆ = ( 1) + ( 1) + ( 1) = 53,888, ( ) + 800( ) + 00(1, ) 53,888,888.89(3 1) aˆ = ,300 1,300 = 157, ,888, k = = , Z = = ,
48 Question #18 Key: D n m α 1 α Let α j be the parameter for region j. The likelihood function is L = α 1+ 1 α+ 1. The i= 1 xi i= 1 yi α α1 expected values satisfy = 1.5 α 1 α1 1 and so 3α 1 α =. Substituting this in the likelihood + α1 function and taking logs produces n m 3α 1 + 4α1 l( α1) = ln L( α1) = nln α1 ( α1+ 1) lnxi + mln ln yi i= 1 + α1 + α1 i= 1 n m n m 6 l'( α1) = ln xi + ln y 0. i = α α ( + α ) ( + α ) 1 i= i= 1 Question #19 Key: A The unrestricted model has ESS = 4,053 +,087 = 6,140 and the restricted model has ESS = 10,374. The unrestricted model has 37 observations and 4 parameters while the restricted model has 37 observations and 8 parameters. The test statistic has 8 4 = 4 numerator degrees of freedom and 37 8 = 9 denominator degrees of freedom. The test statistic is 10,374 6,140 F = 4 = 5. The critical value is about.7 and so the value is statistically 6,140 9 significant. Question #0 Key: D Let Ky ( x ) be the contribution at x of the data point at y. It is 0, x< y 1.4 x y Ky ( x) =, y 1.4 x y , x> y For the particular points, K(4) = 1, K3.3(4) = = 0.75, K4(4) = 0.5, K4.7(4) = 0.5. The kernel estimate is the.8 weighted average 1 (1) + (0.75) + (0.5) + 3 (0.5) =
49 Question #1 Key: B The mean is mq and the variance is mq(1 q). The mean is 34,574 and so the full credibility standard requires the confidence interval to be ± which must be 1.96 standard deviations. Thus, = 1.96 mq(1 q) = ,574 1 q 1 q= 0.9, q= 0.1. Question # Key: A Only the KolmogorovSmirnov test statistic tends toward zero as the sample size goes to infinity. As a consequence, the critical value for the KS statistic has the square root of the sample size in the denominator. For the AndersonDarling and the Chisquare goodnessoffit test statistics, the sample size appears in the numerator of the test statistics themselves. The Schwarz Bayesian Criterion involves an adjustment to the likelihood function, which does not go to zero as the sample size goes to infinity. Question #3 Key: A The adjustment is to divide the regression equation by the square root of the multiplier in the variance. The new equation is Yi β Xi εi = + X i Xi Xi ' ' Y ' i = βxi + εi. The least squares solution is Yi ' ' X i ˆ XY i i X Yi 30.1 β = = i = = =.408. ' X X X 1.5 i i i
50 Question #4 Key: E The sample average is ( )/8 = 96. The model average θ θ , 50 is EX ( 150) = x dx+ 150 dx θ = + = The equation to 150 θ θ θ θ 11, 50 11, 50 11, 50 solve is , 54, θ θ = θ = = 54 = Question #5 Key: C EN ( 1) = 5, EN ( ) = 8(0.55) = 4.4, µ = 0.5(5) + 0.5(4.4) = 4.7 VarN ( 1) = 5, VarN ( ) = 8(0.55)(0.45) = 1.98, v= 0.5(5) + 0.5(1.98) = 3.49 a= 0.5(5) + 0.5(4.4) 4.7 = 0.09, k = 3.49 / 0.09 = r Z = = , = (4.7) The solution is r = 3. Question #6 Key: A These observations are truncated at 500. The contribution to the likelihood function is 1 x / θ f( x) θ e =. Then the likelihood function is 500/ θ 1 F(500) e 1 600/ θ 1 700/ θ 1 900/ θ θ e θ e θ e 3 700/ θ L( θ) = = θ e 500/ θ 3 ( e ) 1 l( θ) = ln L( θ) = 3lnθ 700θ 1 l '( θ) = 3θ + 700θ = 0 θ = 700 / 3 =
51 Question #7 Key: B In 199, D t = 1 and the equation is E(ln Yt) = ( β1 β4ln Xt β 0 5ln X3t ) + ( β 0 + β4) ln Xt + ( β3+ β5)ln X3t. Elasticity is the percent change in Y due to a 1% change in X. Let x be a given value of X t. Then the expected value of lny t is s lnx where s represents the rest of the equation and 0.53 is the estimated value of β + β 4. With a 1% increase in x, the new value is s ln(1.01x) which is the old value plus 0.53ln(1.01) = Exponentiating indicates that the new Y value will be e = times the old value. This is a 0.53% increase, and so the elasticity is Most texts note that the Taylor series approximation indicates that the coefficient itself is a reasonable estimate of the elasticity. So going directly to β + β 4 = 0.53 is a reasonable way to proceed to the answer. Question #8 Key: A For group A let the hazard rate function be the baseline function, h 0 (x). For group B let the hazard rate function be h 0 (x)e β. Then the partial likelihood function is β β β 1 e e 1 e L = =. Taking logarithms and differentiating leads to β β β β β 11+ e 1+ e + e (1+ e )(1 + e ) β β l = β ln() ln(1 + e ) ln(1 + e ) β β e e l ' = = 0 β β 1+ e 1+ e β β β β β β (1+ e )(1 + e ) = e (1 + e ) + e (1+ e ) β β β β β β 1+ 3e + e = e + e + e + e β β 0= e e 1 β 1± 1+ 4 e = = where only the positive root can be used. Because the value is greater than 1, group B has a higher hazard rate function than group A. Therefore, its cumulative hazard rate must also be higher.
52 Question #9 Key: E 0.5 For a compound Poisson distribution, S, the mean is ES ( λµσ,, ) = λex ( ) = λ e µ + σ and the variance is Var( S,, ) E( X ) e µ + λµσ = λ = λ σ. Then, µ + 0.5σ ( ) = [ (,, )] = ES EES λ µσ λe σdλ dµ dσ µ + 0.5σ 0.5σ ( 1) = e σ dµ dσ = e e σdσ = ( e 1)( e 1) = µ + σ [ (,, )] v= E Var S λ µσ = λe σdλ dµ dσ µ + σ σ 0.5( 1) = e σ dµ dσ = e e σdσ = 0.5( e 1)0.5( e 1) = 0.15( e 1) = µ + σ a= VarES [ ( λµσ,, )] = λe σdλ dµ dσ ES ( ) = = = e e E S = e e E S = k = = µ + σ σ e σ dµ dσ E( S) ( e 1) e σdσ E( S) ( 1) ( 1) ( ) ( 1)( 1) / 6 ( ) Question #30 Key: D τ ( θ /1.8) ( θ /1.66) The equations to solve are 0.4 = e, 0.8 = e. Taking logs yields τ τ = ( θ /1.8), = ( θ /1.66). Taking the ratio of the first equation to the second τ τ equation gives = (1.66 /1.8) = Taking logs again, = τ and then τ = Returning to the first (logged) equation, τ = ( θ /1.8), = θ /1.8, θ = τ Question #31 Key: B The number of degrees of freedom for the BoxPierce test is K p q.
53 Question #3 Key: C There are n/ observations of N = 0 (given N = 0 or 1) and n/ observations of N = 1 (given N = 0 or 1). The likelihood function is λ n/ λ n/ n/ nλ n/ e λe λ e λ L = λ λ λ λ = =. Taking logs, differentiating and λ λ n n e + λe e + λe ( e + λe ) (1 + λ) solving provides the answer. l = ln L= ( n/ ) ln λ nln(1 + λ) n n l ' = = 0 λ 1+ λ n(1 + λ) nλ = 0 1 λ = 0, λ = 1. Question #33 Key: D The posterior density function is proportional to the product of the likelihood function and prior density. That is, π( q 1,0) f(1 q) f(0 q) π( q) q(1 q) q = q q. To get the exact posterior density, integrate this function over its range: q q q q q q dq= = and so π ( q 1,0) =. Then, q q Pr(0.7 < q< 0.8 1, 0) = dq= Question #34 Key: B The likelihood function, its logarithm, derivative and solution are 5 5 β xi yi e ( β xi ) L( β ) = p( yi ) = y! i= 1 i= 1 i 5 l( β) = ln L( β) = [ βx + y ln( β) + y ln( x ) ln( y!)] 5 i= 1 i= 1 i i i i i l'( β) = ( x + y / β) = / β = 0, ˆ β = 9 /163. i i To approximate the variance, l''( β ) = yi / β, E yi / β = xiβ / β = 163 / β. i= 1 i= 1 The variance is the reciprocal. i= 1 Substituting the estimate gives ˆ β /163 = 9 /163 = The standard error is the square root,
54 Question #35 Key: B Replace increases with decreases to make this statement true. Question #36 Key: B The cumulative hazard function for the exponential distribution is H(x) = x/θ. The maximum likelihood estimate of θ is the sample mean, which equals (17/15) = Therefore H ˆ (75) = (75/81.8) = To calculate H ˆ 1 (75) use the following table. j y j s j r j Therefore, H ˆ 1 (75) = = Thus, Hˆ ˆ (75) H1(75) = = Question #37 Key: A The sample mean is 0(000) + 1(600) + (300) + 3(80) + 4(0) = = ˆ µ = vˆ and the 3000 sample variance is 000(0 ˆ µ ) + 600(1 ˆ µ ) + 300( ˆ µ ) + 80(3 ˆ µ ) + 0(4 ˆ µ ) = Then, aˆ = = , k = = and Z = =
55 Question #38 Key: E The cdf is 1 F x t dt t. ( ) x 4(1 ) 5 (1 ) 4 x = + = + = (1 + ) 4 x Observation (x) F(x) compare to: Maximum difference , , , , , KS statistic is Question #39 Key: B When forecasting, assume that all future values of the error term are zero. y ˆ T+ 1 = 0.9yT + 1+ εt εt, yt+ 1 = 0.9(8) (0.5) = 8 y = 0.9y + 1+ ε 0.4 ε, yˆ = 0.9(8) (0) = 8.. T+ T+ 1 T+ T+ 1 T+ Question #40 Key: D This follows from the formula MSE( ˆ θ ) = Var( ˆ θ) + [ bias( ˆ θ)]. If the bias is zero, then the meansquared error is equal to the variance.
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