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![Page 1: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/1.jpg)
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GLM I: Introduction to Generalized Linear Models
GLM I: Introduction to Generalized Linear Models
By Curtis Gary DeanDistinguished Professor of Actuarial Science
Ball State University
By Curtis Gary DeanDistinguished Professor of Actuarial Science
Ball State University
![Page 2: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/2.jpg)
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Classical Multiple Linear RegressionClassical Multiple Linear Regression
Yi = a0 + a1Xi1 + a2Xi2 …+ amXim + ei
Yi are the response variables Xij are predictors
i subscript denotes ith observation
j subscript identifies jth predictor
Yi = a0 + a1Xi1 + a2Xi2 …+ amXim + ei
Yi are the response variables Xij are predictors
i subscript denotes ith observation
j subscript identifies jth predictor
![Page 3: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/3.jpg)
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One Predictor: Yi = a0 + a1Xi One Predictor: Yi = a0 + a1Xi
60
65
70
75
60 65 70 75
Average Height of Parents (Inches)
Hei
gh
t o
f O
ldes
t S
on
60
65
70
75
60 65 70 75
Average Height of Parents (Inches)
Hei
gh
t o
f O
ldes
t S
on
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Classical Multiple Linear Regression: Solving for ai
Classical Multiple Linear Regression: Solving for ai
n
iimmii
m
XaXaaY
aaaG
1
2110
10
)(
),..,( Minimize
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Classical Multiple Linear RegressionClassical Multiple Linear Regression
μi = E[Yi] = a0 + a1Xi1 + …+ amXim
Yi is Normally distributed random variable with constant variance σ2
Want to estimate μi = E[Yi] for each i
μi = E[Yi] = a0 + a1Xi1 + …+ amXim
Yi is Normally distributed random variable with constant variance σ2
Want to estimate μi = E[Yi] for each i
![Page 6: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/6.jpg)
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Response Yi has Normal Distribution
-12 -7 -2 3 8 μ i
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Problems with Traditional ModelProblems with Traditional Model
Number of claims is discrete
Claim sizes are skewed to the right
Probability of an event is in [0,1]
Variance is not constant across data points i
Nonlinear relationship between X’s and Y’s
Number of claims is discrete
Claim sizes are skewed to the right
Probability of an event is in [0,1]
Variance is not constant across data points i
Nonlinear relationship between X’s and Y’s
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Generalized Linear Models - GLMsGeneralized Linear Models - GLMs
Fewer restrictions
Y can model number of claims, probability of renewing, loss severity, loss ratio, etc.
Large and small policies can be put into one model
Y can be nonlinear function of X’s
Classical linear regression model is a special case
Fewer restrictions
Y can model number of claims, probability of renewing, loss severity, loss ratio, etc.
Large and small policies can be put into one model
Y can be nonlinear function of X’s
Classical linear regression model is a special case
![Page 9: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/9.jpg)
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Generalized Linear Models - GLMsGeneralized Linear Models - GLMs
Same goal
Predict : μi = E[Yi]
Same goal
Predict : μi = E[Yi]
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Generalized Linear Models - GLMsGeneralized Linear Models - GLMs
g(μi )= a0 + a1Xi1 + …+ amXim
g( ) is the link function
E[Yi] = μi = g-1(a0 + a1Xi1 + …+ amXim)
Yi can be Normal, Poisson, Gamma, Binomial, Compound Poisson, …
Variance can be modeled
g(μi )= a0 + a1Xi1 + …+ amXim
g( ) is the link function
E[Yi] = μi = g-1(a0 + a1Xi1 + …+ amXim)
Yi can be Normal, Poisson, Gamma, Binomial, Compound Poisson, …
Variance can be modeled
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GLMs Extend Classical Linear RegressionGLMs Extend Classical Linear Regression
If link function is identity: g(μi) = μi
And Yi has Normal distribution
→ GLM gives same answer as Classical Linear Regression*
* Least squares and MLE equivalent for Normal dist.
If link function is identity: g(μi) = μi
And Yi has Normal distribution
→ GLM gives same answer as Classical Linear Regression*
* Least squares and MLE equivalent for Normal dist.
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Exponential Family of Distributions – Canonical Form
Exponential Family of Distributions – Canonical Form
parameter. nuisancea called often is
!interest ofparameter theis
)()(''][
)('][
,exp , ;
abYVar
bYE
yca
byyf
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Some Math Rules: RefresherSome Math Rules: Refresher
yxyx
xxx
xrx
yxxy
xxx
ex
r
x
lnln)/ln()6(
ln)ln()/1ln()5(
ln)ln()4(
lnln)ln()3(
]ln[exp]exp[ln)2(
)exp()1(
1
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Normal Distribution in Exponential FamilyNormal Distribution in Exponential Family
22
2
2
2
2
22
2
2
2
2
2
2ln2
2/exp
2
2exp
2
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)(exp
2
1),;(
yy
yy
yyf
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Normal Distribution in Exponential FamilyNormal Distribution in Exponential Family
22
2
2
22
2
2
22
1)()(][ and
)(2/)(then
,)( and Let
2ln2
2/exp),;(
abYVar
bb
a
yyyf
θ b(θ)
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Poisson Distribution in Exponential FamilyPoisson Distribution in Exponential Family
)!ln(1
)(lnexp]Pr[
!lnexp]Pr[
!]Pr[
yy
yY
y
eyY
y
eyY
y
y
)!ln(1
)(lnexp]Pr[
!lnexp]Pr[
!]Pr[
yy
yY
y
eyY
y
eyY
y
y
θ
![Page 17: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/17.jpg)
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Poisson Distribution in Exponential FamilyPoisson Distribution in Exponential Family
ed
dabYVar
eed
dbYE
aeb
e
2
2
)()(''][
)('][
1)(and)(
ln
ed
dabYVar
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dbYE
aeb
e
2
2
)()(''][
)('][
1)(and)(
ln
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Compound Poisson DistributionCompound Poisson Distribution
Y = C1 + C2 + . . . + CN
N is Poisson random variable Ci are i.i.d. with Gamma distribution
This is an example of a Tweedie distribution
Y is member of Exponential Family
Y = C1 + C2 + . . . + CN
N is Poisson random variable Ci are i.i.d. with Gamma distribution
This is an example of a Tweedie distribution
Y is member of Exponential Family
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Members of the Exponential FamilyMembers of the Exponential Family
• Normal• Poisson• Binomial• Gamma• Inverse Gaussian• Compound Poisson
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Variance StructureVariance Structure
E[Yi] = μi = b' (θi) → θi = b' (-1)(μi )
Var[Yi] = a(Φi) b' ' (θi) = a(Φi) V(μi)
Common form: Var[Yi] = Φ V(μi)/wi
Φ is constant across data but weights applied to data points
E[Yi] = μi = b' (θi) → θi = b' (-1)(μi )
Var[Yi] = a(Φi) b' ' (θi) = a(Φi) V(μi)
Common form: Var[Yi] = Φ V(μi)/wi
Φ is constant across data but weights applied to data points
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V(μ) Normal 0
Poisson Binomial (1-) Tweedie p, 1<p<2 Gamma 2
Inverse Gaussian 3
Recall: Var[Yi] = Φ V(μi)/wi
Variance Functions V(μ)
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Variance at Point and Fit Variance at Point and Fit
A Practioner’s Guide to Generalized Linear Models: A CAS Study Note
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Variance of Yi and Fit at Data Point iVariance of Yi and Fit at Data Point i
Var(Yi) is big → looser fit at data point i
Var(Yi) is small → tighter fit at data point i
Var(Yi) is big → looser fit at data point i
Var(Yi) is small → tighter fit at data point i
)Var(
1fit of Tightness
iY
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Why Exponential Family?Why Exponential Family?
Distributions in Exponential Family can model a variety of problems
Standard algorithm for finding coefficients a0, a1, …, am
Distributions in Exponential Family can model a variety of problems
Standard algorithm for finding coefficients a0, a1, …, am
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Modeling Number of ClaimsModeling Number of Claims
Number ofPolicy Sex Territory Claims in 5 Years
1 M 02 02 F 01 03 F 01 04 F 02 15 F 01 06 F 02 17 M 02 28 M 02 29 M 02 1
10 F 01 1: : : :
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Assume a Multiplicative ModelAssume a Multiplicative Model
μi = expected number of claims in five years
μi = BF,01 x CSex(i) x CTerr(i)
If i is Female and Terr 01
→ μi = BF,01 x 1.00 x 1.00
μi = expected number of claims in five years
μi = BF,01 x CSex(i) x CTerr(i)
If i is Female and Terr 01
→ μi = BF,01 x 1.00 x 1.00
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Multiplicative ModelMultiplicative Model
μi = exp(a0 + aSXS(i) + aTXT(i))
μi = exp(a0) x exp(aSXS(i)) x exp( aTXT(i))
i is Female → XS(i) = 0; Male → XS(i) = 1
i is Terr 01 → XT(i) = 0; Terr 02 → XT(i) = 1
μi = exp(a0 + aSXS(i) + aTXT(i))
μi = exp(a0) x exp(aSXS(i)) x exp( aTXT(i))
i is Female → XS(i) = 0; Male → XS(i) = 1
i is Terr 01 → XT(i) = 0; Terr 02 → XT(i) = 1
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Values of Predictor VariablesValues of Predictor Variables
Policy Sex XS(i) Territory XT(i)
1 M 1 02 12 F 0 01 03 F 0 01 04 F 0 02 15 F 0 01 06 F 0 02 17 M 1 02 18 M 1 02 19 M 1 02 1
10 F 0 01 0
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Natural Log Link FunctionNatural Log Link Function
ln(μi) = a0 + aSXS(i) + aTXT(i)
μi is in (0 , ∞ )
ln(μi) is in ( - ∞ , ∞ )
ln(μi) = a0 + aSXS(i) + aTXT(i)
μi is in (0 , ∞ )
ln(μi) is in ( - ∞ , ∞ )
![Page 30: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/30.jpg)
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Poisson Distribution in Exponential FamilyPoisson Distribution in Exponential Family
eb
yy
yY
)(
ln
)!ln(1
lnexp]Pr[
eb
yy
yY
)(
ln
)!ln(1
lnexp]Pr[
![Page 31: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/31.jpg)
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Natural Log is Canonical Link for PoissonNatural Log is Canonical Link for Poisson
θi = ln(μi)
θi = a0 + aSXS(i) + aTXT(i)
θi = ln(μi)
θi = a0 + aSXS(i) + aTXT(i)
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Estimating Coefficients a1, a2, .., amEstimating Coefficients a1, a2, .., am
Classical linear regression uses least squares
GLMs use Maximum Likelihood Method
Solution will exist for distributions in exponential family
Classical linear regression uses least squares
GLMs use Maximum Likelihood Method
Solution will exist for distributions in exponential family
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Likelihood and Log LikelihoodLikelihood and Log Likelihood
n
iii
n
iii
yfy
yLy
yfyL
111
1111
111
);(ln,..),..;(
,..)],..;(ln[,..),..;(
);(,..),..;(
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Find a0, aS, and aT for PoissonFind a0, aS, and aT for Poisson
)()(0
111
with
!ln,...),..;(
:Maximize
iTTiSSi
n
iiii
xaxaa
yeyy i
![Page 35: 1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.](https://reader035.fdocuments.net/reader035/viewer/2022062408/56649f125503460f94c25395/html5/thumbnails/35.jpg)
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Iterative Numerical Procedure to Find ai’sIterative Numerical Procedure to Find ai’s
Use statistical package or actuarial software
Specify link function and distribution type
“Iterative weighted least squares” is the numerical method used
Use statistical package or actuarial software
Specify link function and distribution type
“Iterative weighted least squares” is the numerical method used
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Solution to Our ExampleSolution to Our Example
a0 = -.288 → exp(-.288) = .75 aS = .262 → exp(.262) = 1.3 aT = .095 → exp(.095) = 1.1
μi = exp(a0) x exp(aSXS(i)) x exp( aTXT(i))
μi = .75 x 1.3XS(i) x 1.1XT(i)
i is Male,Terr 01 → μi = .75 x 1.31 x 1.10
a0 = -.288 → exp(-.288) = .75 aS = .262 → exp(.262) = 1.3 aT = .095 → exp(.095) = 1.1
μi = exp(a0) x exp(aSXS(i)) x exp( aTXT(i))
μi = .75 x 1.3XS(i) x 1.1XT(i)
i is Male,Terr 01 → μi = .75 x 1.31 x 1.10
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Testing New Drug TreatmentTesting New Drug Treatment
X1 X2 Y
Dosage Age Cure Value1.0 30 Yes 11.0 43 No 01.0 82 No 01.5 45 No 01.5 67 No 01.5 26 Yes 12.0 33 Yes 12.0 50 Yes 12.0 72 No 02.5 31 Yes 12.5 45 Yes 12.5 75 Yes 1
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Multiple Linear RegressionMultiple Linear Regression
Cure ActualPredicted
ProbabilityYes 1 0.5179No 0 0.3298No 0 -0.2345No 0 0.5366No 0 0.2183
Yes 1 0.8115Yes 1 0.9460Yes 1 0.7000No 0 0.3817
Yes 1 1.2107Yes 1 1.0081Yes 1 0.5740
Dependent Variable: Y
Cure ActualPredicted
ProbabilityYes 1 0.5179No 0 0.3298No 0 -0.2345No 0 0.5366No 0 0.2183
Yes 1 0.8115Yes 1 0.9460Yes 1 0.7000No 0 0.3817
Yes 1 1.2107Yes 1 1.0081Yes 1 0.5740
Dependent Variable: Y
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Logistic Regression ModelLogistic Regression Model
p = probability of cure, p in [0,1]
odds ratio: p/(1-p) in [0, + ∞ ]
ln[p/(1-p)] in [ - ∞ , + ∞ ]
ln[p/(1-p)] = a + b1X1 + b2X2
p = probability of cure, p in [0,1]
odds ratio: p/(1-p) in [0, + ∞ ]
ln[p/(1-p)] in [ - ∞ , + ∞ ]
ln[p/(1-p)] = a + b1X1 + b2X2
Link function
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Logistic Regression ModelLogistic Regression Model
X1 X2
Dosage Age Cure ValuePredicted
Probability1.0 30 Yes 1 0.5681.0 43 No 0 0.0001.0 82 No 0 0.0001.5 45 No 0 0.6481.5 67 No 0 0.0001.5 26 Yes 1 1.0002.0 33 Yes 1 1.0002.0 50 Yes 1 1.0002.0 72 No 0 0.0002.5 31 Yes 1 1.0002.5 45 Yes 1 1.0002.5 75 Yes 1 0.784
Dependent Variable YX1 X2
Dosage Age Cure ValuePredicted
Probability1.0 30 Yes 1 0.5681.0 43 No 0 0.0001.0 82 No 0 0.0001.5 45 No 0 0.6481.5 67 No 0 0.0001.5 26 Yes 1 1.0002.0 33 Yes 1 1.0002.0 50 Yes 1 1.0002.0 72 No 0 0.0002.5 31 Yes 1 1.0002.5 45 Yes 1 1.0002.5 75 Yes 1 0.784
Dependent Variable Y
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Which Exponential Family Distribution?Which Exponential Family Distribution?
Frequency: Poisson, {Negative Binomial}
Severity: Gamma
Loss ratio: Compound Poisson Pure Premium: Compound Poisson
How many policies will renew: Binomial
Frequency: Poisson, {Negative Binomial}
Severity: Gamma
Loss ratio: Compound Poisson Pure Premium: Compound Poisson
How many policies will renew: Binomial
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What link function?What link function?
Additive model: identity
Multiplicative model: natural log
Modeling probability of event: logistic
Additive model: identity
Multiplicative model: natural log
Modeling probability of event: logistic
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