# SAS/STAT Examples

## Fitting Tweedie’s Compound Poisson-Gamma Mixture Model by Using PROC HPGENSELECT

Contents | SAS Program | PDF

## Overview

Semicontinuous random variables are characterized by a continuous distribution that has point masses at one or more locations. One way to model semicontinuous data is to fit a generalized linear model by using a Tweedie distribution for the response variable. Tweedie distributions have been used in such diverse fields as actuarial science, economics, telecommunications, ecology, medicine, and meteorology. This example illustrates how to fit a Tweedie model to aggregate insurance claims payments data by using the HPGENSELECT procedure which is available in SAS/STAT 12.3, recently released with SAS 9.4.

## Analysis

Exponential dispersion models are the response distributions for generalized linear models. Any exponential dispersion model can be characterized by its variance function , which describes the mean-variance relationship of the distribution when the dispersion is held constant. If Y follows an exponential dispersion model distribution that has mean , variance function , and dispersion , then the variance of Y can be written as

Tweedie distributions are a special case of the exponential dispersion family for which and (Dunn and Smyth, 2005). The distribution is defined for all values of except values of in the open interval . Many important known distributions are a special case of Tweedie distributions including normal ( ), Poisson (), gamma (), and inverse Gaussian (). Apart from these special cases, the probability density unction of the Tweedie distribtion does not have an analyticl expression. For , it has the form

where for and for . The function does not have an analytical expression. It is usually evaluated by using the series expansion methods that are described in Dunn and Smyth (2005).

For , the Tweedie distribution is a compound Poisson-gamma mixture distribution, which is the distribution of defined as

where and are independently and identically distributed gamma random variables with the shape parameter and the scale parameter . At , the density is a probability mass that is governed by the Poisson distribution, and for values of , the density is a mixture of gamma variates with Poisson mixing probability. The parameters , , and are related to the natural parameters , , and of the Tweedie distribution as

The mean of a Tweedie distribution is positive for .

## Example: Modeling Insurance Claims Data

When modeling aggregate payments from insurance claims, if you assume that the arrival of claims follows a Poisson distribution, that the size of individual claims are independently and identically gamma distributed, and that the arrival and sizes are independent of one another, then the aggregate payments follow a Tweedie compound Poisson-gamma mixture distribution (Frees, 2010). This example uses PROC HPGENSELECT to fit a Tweedie model to the aggregate loss data from a Swedish study about third-party automobile insurance claims for 1977. The data were compiled by the Swedish Committee on the Analysis of Risk Premium in Motor Insurance (Andrews and Herzberg, 1985). The following SAS statements create the data set MotorIns, and Table 1 describes the variables:

data motorins;
input Kilometres Zone Bonus Make Insured Claims Payment;
Zeros=ifn(payment ne 0,1,0);
datalines;
1 1 1 1 455.13 108 392491
1 1 1 2 69.17 19 46221
1 1 1 3 72.88 13 15694
1 1 1 4 1292.39 124 422201
1 1 1 5 191.01 40 119373
1 1 1 6 477.66 57 170913

... more lines ...

5 7 7 8 13.06 0 0
5 7 7 9 384.87 16 112252
;
run;


Table 1: Example Data Set MotorIns

Variable

Type

Description

Kilometres

Class

Distance traveled

Zone

Class

Geographical zone

Make

Class

Make of automobile

Bonus

Continuous

No-claims bonus

Insured

Continuous

Number of insured drivers (years 100,000)

Continuous

Natural logarithm of Insured

Claims

Continuous

Number of insurance claims

Payment

Continuous

Sum of insurance claims payments, in Swedish kronor

Zeros

Binary

Indicator variable for Payment not equal to 0

Table 2 describes the levels of the classification variable Kilometres.

Table 2: Values and Labels of Kilometres

Value

Label

1

Less than 1,000 km per year

2

1,000–15,000 km per year

3

15,000–20,000 km per year

4

20,000–25,000 km per year

5

More than 25,000 km per year

Table 3 describes the levels of the classification variable Zone. The zones are given from a detailed investigation of 100 areas in 1972 and represent combinations of traffic intensity, state of roads, climatic differences, and so on (Andrews and Herzberg, 1985).

Table 3: Values and Labels of Zone

Value

Label

1

Stockholm, Göteborg, Malmö with surroundings

2

Other large cities with surroundings

3

Smaller cities with surroundings in southern Sweden

4

Rural areas in southern Sweden

5

Smaller cities with surroundings in northern Sweden

6

Rural areas in northern Sweden

7

Gotland

The models of cars are classified into 10 premium classes, but in a special investigation for 1977 eight common pure models were chosen and the rest were put in a combined class for reference (Andrews and Herzberg, 1985). The levels 1–8 of the classification variable Make represent the eight pure models, and level 9 is the combined class.

The variable Bonus is a measure of individual claim history. The insured motorist starts in the class Bonus = 1. Every year that no claim is filed, the insured moves up one class (Andrews and Herzberg, 1985).

Figure 1 shows a histogram of the response variable Payment and the proportion of zeros. The variable Payment exhibits a distribution that is fairly typical of semicontinuous variables: a significant density mass at zero and a continuous, right-skewed distribution elsewhere.

Figure 1: Distribution of Payment

Output 1: Frequency of Zeros

The FREQ Procedure

Zeros Frequency Percent Cumulative
Frequency
Cumulative
Percent
0 385 17.64 385 17.64
1 1797 82.36 2182 100.00

The following SAS statements fit a Tweedie compound Poisson-gamma mixture model to the response variable Payment.

The CLASS statement specifies that the variables Kilometres, Zone, and Make are categorical variables. The SPLIT option requests that the columns of the design matrix that correspond to any effect that contains a split classification variable be able to be selected to enter or leave a model independently of the other design columns of that effect. The PARAM= option specifies a reference cell encoding for the classification variables.

The MODEL statement specifies that the response variable have a Tweedie distribution with a log link function. The candidates for the linear predictor include the main effects and the interactions between the classification variables Kilometres, Zone, and Make and the continuous variable Bonus. The OFFSET= option specifies that the variable LogInsured be included in the linear predictor with a coefficient of 1.

The SELECTION statement requests that stepwise selection be used and that the final model be chosen based on the AICC criterion. The DETAILS=SUMMARY option requests that only a summary of the selection process be displayed rather than the details from each step of the selection process.

The OUTPUT statement requests that the selected model’s prediction and residuals be saved to the SAS data set Tweedie.

The ID statement requests that the variable Payment also be included in the output data set.

proc hpgenselect data=motorins;
class Kilometres Zone Make / split param=reference;
model payment = Kilometres|Zone|Make|Bonus /
selection method=stepwise(choose=aicc) details=summary;
output out=tweedie P R;
id payment;
run;


The Performance Information table in Output 2 shows that the procedure executed in single-machine mode (that is, on the server where SAS is installed). When high-performance procedures run in single-machine mode, they use concurrently scheduled threads. In this case, four threads were used.

The Model Information table reports that a Tweedie model was fit with a log link function.

The Selection Information table reports that stepwise selection was used, the selection and stopping criteria are the significance level of each individual effect, the entry significance level is the default value of 0.05, and the choose criterion is AICC.

Output 2: Performance, Model, and Selection Information

The HPGENSELECT Procedure

Performance Information
Execution Mode Single-Machine
Model Information
Data Source WORK.MOTORINS
Response Variable Payment
Class Parameterization Reference
Distribution Tweedie
Optimization Technique Quasi-Newton

Selection Information
Selection Method Stepwise
Select Criterion Significance Level
Stop Criterion Significance Level
Choose Criterion AICC
Effect Hierarchy Enforced None
Entry Significance Level (SLE) 0.05
Stay Significance Level (SLS) 0.05
Stop Horizon 1

Output 3 shows that the sample size is 2,182, and the Class Level Information table shows the number of levels and the level values of the three classification variables.

Output 3: Sample Size and Classification Variable Levels

 Number of Observations Read 2182 2182

Class Level Information
Class Levels   Reference
Value
Values
Kilometres 5 * 5 1 2 3 4 5
Zone 7 * 7 1 2 3 4 5 6 7
Make 9 * 9 1 2 3 4 5 6 7 8 9

 * Associated Parameters Split

The Selection Summary table in Output 4 reports the variables that are added at each step of the selection process. The summary shows that 30 effects plus an intercept were selected and that the selection process terminated because the sequence of effect additions and removals began cycling.

Output 4: Effect Selection Summary

The HPGENSELECT Procedure

Selection Summary
Step Effect
Entered
Effect
Removed
Number
Effects In
AICC p Value
0 Intercept   1 44299.3018 .
1 Bonus   2 43694.2249 <.0001
2 Zone_1   3 43576.8235 <.0001
3 Kilometres_1   4 43414.2670 <.0001
4 Make_4   5 43251.5268 <.0001
5 Make_6   6 43186.2263 <.0001
6 Bonus*Kilometres_2   7 43128.0129 <.0001
7 Zone_5*Make_8   8 43103.4200 <.0001
8 Bonus*Kilometres_2*Zone_1*Make_8   9 43087.3135 <.0001
9 Zone_2   10 43057.5628 <.0001
10 Bonus*Kilometres_3   11 43035.8693 <.0001
11 Make_5   12 43021.0275 <.0001
12 Kilometres_1*Make_1   13 43010.8831 0.0003
13 Make_8   14 43000.7509 0.0003
14 Make_2   15 42990.3324 0.0003
15 Bonus*Kilometres_4*Zone_6*Make_4   16 42987.4687 0.0007
16 Kilometres_1*Zone_5*Make_7   17 42981.7743 0.0008
17 Kilometres_1*Zone_6*Make_7   18 42976.6767 0.0019
18 Bonus*Zone_5   19 42969.6789 0.0020
19 Bonus*Zone_3   20 42961.8306 0.0015
20 Bonus*Kilometres_1*Make_1   21 42954.6503 0.0019
21 Kilometres_4   22 42945.4183 0.0008
22 Make_1   23 42937.8402 0.0017
23 Bonus*Make_5   24 42931.7907 0.0039
24 Bonus*Kilometres_1   25 42925.8663 0.0045
25 Zone_2*Make_6   26 42920.2139 0.0075
26 Bonus*Kilometres_1*Zone_6   27 42915.7147 0.0082
27 Bonus*Kilometres_3*Zone_5*Make_2   28 42913.6024 0.0183
28 Bonus*Kilometres_4*Zone_1*Make_5   29 42911.4418 0.0190
29 Zone_1*Make_2   30 42908.4035 0.0202
30 Bonus*Zone_1*Make_2   31 42904.3219* 0.0099
31   Kilometres_1*Zone_6*Make_7 30 42907.6359 0.0534

 * Optimal Value of Criterion

 Stepwise selection stopped because the sequence of effect additions and removals is cycling.

 The model at step 30 is selected where AICC is 42904.32.

The Selected Effects note in Output 5 lists the effects that are selected for the final model. The Dimensions table reports that 31 effects are included in the final model and 33 parameters are estimated. The Fit Statistics table reports that the value of AICC for the final model is 42,904.

Output 5: Selected Effects, Dimensions, Convergence Status, and Fit Statistics

Selected Effects: Intercept Kilometres_1 Kilometres_4 Zone_1 Zone_2 Make_1 Make_2 Make_4 Make_5 Make_6 Make_8 Kilometres_1*Make_1 Zone_1*Make_2 Zone_2*Make_6 Zone_5*Make_8 Kilometres_1*Zone_5*Make_7 Kilometres_1*Zone_6*Make_7 Bonus Bonus*Kilometres_1 Bonus*Kilometres_2 Bonus*Kilometres_3 Bonus*Zone_3 Bonus*Zone_5 Bonus*Kilometres_1*Zone_6 Bonus*Make_5 Bonus*Kilometres_1*Make_1 Bonus*Zone_1*Make_2 Bonus*Kilometres_2*Zone_1*Make_8 Bonus*Kilometres_3*Zone_5*Make_2 Bonus*Kilometres_4*Zone_1*Make_5 Bonus*Kilometres_4*Zone_6*Make_4

Dimensions
Number of Effects 31
Number of Effects after Splits 31
Number of Parameters 33
Columns in X 31

Fit Statistics
-2 Log Likelihood 42837
AIC (smaller is better) 42903
AICC (smaller is better) 42904
BIC (smaller is better) 43091
Pearson Chi-Square 1015375
Pearson Chi-Square/DF 472.04777

 Convergence criterion (GCONV=1E-8) satisfied.

Output 6 displays the estimates of the model parameters. The estimate of the dispersion parameter, , is 349.65 and the estimate of the power, , is 1.36. The effect of using the SPLIT option in the CLASS statement is apparent. None of the classification variables have all their main effects or complete sets of interactions included in the model. The result is a more parsimonious model than you would achieve without enabling the design columns to enter and leave the model independently.

Output 6: Parameter Estimates

Parameter Estimates
Parameter DF Estimate Standard
Error
Chi-Square Pr > ChiSq
Intercept 1 6.422717 0.030076 45604.6548 <.0001
Kilometres_1 1 -0.396625 0.050252 62.2944 <.0001
Kilometres_4 1 -0.163970 0.038900 17.7679 <.0001
Zone_1 1 0.431027 0.028089 235.4780 <.0001
Zone_2 1 0.234752 0.028513 67.7853 <.0001
Make_1 1 0.102037 0.031974 10.1842 0.0014
Make_2 1 0.104421 0.048899 4.5601 0.0327
Make_4 1 -0.676695 0.051711 171.2483 <.0001
Make_5 1 0.467489 0.091598 26.0480 <.0001
Make_6 1 -0.200767 0.039391 25.9776 <.0001
Make_8 1 0.240254 0.056656 17.9826 <.0001
Kilometres_1*Make_1 1 0.443491 0.132307 11.2358 0.0008
Zone_1*Make_2 1 0.728101 0.202768 12.8939 0.0003
Zone_2*Make_6 1 -0.286627 0.101679 7.9464 0.0048
Zone_5*Make_8 1 0.520894 0.160295 10.5599 0.0012
Kilometres_1*Zone_5*Make_7 1 0.736407 0.250920 8.6132 0.0033
Kilometres_1*Zone_6*Make_7 1 0.453568 0.230985 3.8558 0.0496
Bonus 1 -0.138215 0.006689 426.9209 <.0001
Bonus*Kilometres_1 1 -0.035637 0.010883 10.7225 0.0011
Bonus*Kilometres_2 1 -0.069006 0.006272 121.0511 <.0001
Bonus*Kilometres_3 1 -0.045452 0.006450 49.6621 <.0001
Bonus*Zone_3 1 0.019307 0.005369 12.9336 0.0003
Bonus*Zone_5 1 0.027528 0.007469 13.5859 0.0002
Bonus*Kilometres_1*Zone_6 1 0.031829 0.012152 6.8603 0.0088
Bonus*Make_5 1 -0.055964 0.018012 9.6539 0.0019
Bonus*Kilometres_1*Make_1 1 -0.063791 0.025037 6.4914 0.0108
Bonus*Zone_1*Make_2 1 -0.107665 0.039459 7.4448 0.0064
Bonus*Kilometres_2*Zone_1*Make_8 1 0.166930 0.043453 14.7578 0.0001
Bonus*Kilometres_3*Zone_5*Make_2 1 0.101962 0.045120 5.1068 0.0238
Bonus*Kilometres_4*Zone_1*Make_5 1 0.098160 0.045496 4.6550 0.0310
Bonus*Kilometres_4*Zone_6*Make_4 1 0.233105 0.094307 6.1097 0.0134
Dispersion 1 349.647997 28.740411 . .
Power 1 1.363043 0.008891 . .

The following SAS statements generate a scatter plot that compares the model predictions with the observed values of the response variable.


proc sort data=tweedie out=tweedie;
by payment;
run;


proc sgplot data=tweedie;
scatter x=payment y=pred / legendlabel="Predicted";
series x=payment y=payment / lineattrs=(pattern=solid color=red)
legendlabel="45 degree line";
yaxis label="Predicted";
run;


Figure 2 shows that the predictions of the final model compare favorably with the observed responses.

Figure 2: Scatter Plot of Predicted versus Observed Payments

## References

• Andrews, D. F. and Herzberg, A. M. (1985), A Collection of Problems from Many Fields for the Student and Research Worker, New York: Springer-Verlag.

• Dunn, P. K. and Smyth, G. K. (2005), “Series Evaluation of Tweedie Exponential Dispersion Model Densities,” Statistics and Computing, 15, 267–280.

• Frees, E. W. (2010), Regression Modeling with Actuarial and Financial Applications, Cambridge: Cambridge University Press.

• Jørgensen, B. and Paes de Souza, M. C. (1994), “Fitting Tweedie’s Compound Poisson Model to Insurance Claims Data,” Scandinavian Actuarial Journal, 1, 69–93.