Flexible smoothing with P-splines: some...

1

Flexible smoothing with P-splines: someapplications

Maria DurbanDepartment of Statistics, Universidad Carlos III de Madrid, Spain

Joint work with Raymon Carroll, Iain Currie, Paul Eilers, Jareck Harezlaand Matt Wand

Department of Economics,Bielefeld University, June 2003

2

What is this talk about?

2


• Introduction

? Smoothing? Why P-splines?? Mixed model representation of P-splines

2


• Introduction


• Applications

? Additive models? Models with heteroscedastic errors? Smoothing and correlation? Generalised additive models

2


• Introduction


• Applications

? Additive models? Models with heteroscedastic errors? Smoothing and correlation? Generalised additive models

• P-splines for longitudinal data

3

Canadian Occupational Prestige Data (B. Blishen, 1971)

Data consist of prestige scores, average income (in $1000) and education(in years) for 102 occupations.

income

pres

tige

0 5000 10000 15000 20000 25000

2040

6080

education

pres

tige

6 8 10 12 14 16

2040

6080

4

Smoothing

• Prestige score varies smoothly along the income range

• A suitable model for these data could be:

y = f(x) + ε

where x is the covariate (income) f is a smooth function of x whichdepends on λ =smoothing parameter

• Smoothing methods fall into two groups:

? Specified by the fitting procedure: Kernels? Solution of a minimisation problem: Splines

5

0 5000 10000 15000 20000 25000

income

020

4060

8010

0

pres

tige

6

P-spline• Eilers and Marx, 1996.

• They are a generalisation of ordinary regression.

• Modify the log-likelihood by a penalty on the regression coefficients.

y = f(x) + ε f(x) ≈ Ba S = (y −Ba)′(y −Ba) + λa′Pa

a = (B′B + λP )−1B′y

6

P-spline• Eilers and Marx, 1996.

• They are a generalisation of ordinary regression.

• Modify the log-likelihood by a penalty on the regression coefficients.

y = f(x) + ε f(x) ≈ Ba S = (y −Ba)′(y −Ba) + λa′Pa

a = (B′B + λP )−1B′y

P-splines receive also other names:

• Penalised splines

• pseudosplines

• low-rank smoothers

7

Basis for P-splines

B-splines, truncated polynomial basis, radial basis, etc.

7

Basis for P-splines


B-splines

• B-spline: bell-shaped like Gauss curve

• Polynomial pieces smoothly joining at the knots

7

Basis for P-splines


B-splines

• B-spline: bell-shaped like Gauss curve

• Polynomial pieces smoothly joining at the knots

Truncated polynomial

For example: truncated linear basis for knots κ1, . . . , κk is:

1,x, (x− κ1)+, . . . , (x− κk)+

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0 10 20 30 40

0.0

0.1

0.2

0.3

0.4

0.5

0.6

B-spline basis

0 10 20 30 40

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Scaled B-splines and their sum

0 10 20 30 40

010

2030

Truncated lines basis

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Why P-splines?

• The number of basis functions used to construct the function estimatesdoes not grow with the sample size

• Quite insensitive to the choice of knots (Ruppert, 2000)

• Computationally simpler

• No need for backfitting in the case of additive models

• Easily extended to 2 or more dimesions and non Gaussian errors

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Psplines: mixed model approach

10

Psplines: mixed model approach

y = f(x) + ε ε ∼ N(0, σ2R)

We write f(x) = Ba. It can be shown that Ba may be written as

Xβ︸︷︷︸fixed

+ Zu︸︷︷︸random

u ∼ N(0, σ2uI) λ = σ2/σ2

u

y = Xβ+Zu+ε Cov

[uε

]=

[σ2

uI 00 σ2R

]Cov[y] = V = Rσ2+Z ′Zσ2

u

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Use REML for variance parameters

l(V ) = −12

log |V |−12

log |X ′V X|−y′(V −1−V −1X(X ′V X)−1X ′V −1)y,

Given R, σ2 and σ2u, β and u are solutions to:[

X ′R−1X X ′R−1ZZ ′R−1X Z′R−1Z + λI

] [βu

]=

[X ′R−1

Z ′R−1

]y.

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Advantages

• Unified approach

• Automatic selection of smoothing parameter

• Likelihood ratio test for model selection

• Already implemented in standard sofware: Splus, SAS, R.

13

APPLICATIONS

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Additive models: Prestige data revisited

y = f(income)︸︷︷︸X1β1+Z1u1

+ f(education)︸︷︷︸X2β2+Z2u2

+ε

= Xβ + Zu + ε Cov

[uε

]=

σ2u1

I 0 00 σ2

u2I 0

0 0 σ2I

β = (β′

1,β′2)

′ u = (u′1,u

′2)

′ X = [X1 : X2] Z = [Z1 : Z2]

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Partial residuals plot

0 5000 10000 15000 20000 25000

income

-20

-10

010

20

part

ial r

esid

uals

6 8 10 12 14 16

education

-20

-10

010

2030

part

ial r

esid

uals

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Is the model additive?: Conditional plots

6 8 10 12 14 16 6 8 10 12 14 16

2040

6080

2040

6080

6 8 10 12 14 16

education

pres

tige

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Two-dimensional P-splines

Now y = f(income, education) + ε = Ba + ε, where

B = B1 ⊗B2 P = λ1P 1 ⊗ In2 + λ2In2 ⊗ P 1

8

10

12

14

education5000

10000

15000

20000

25000

income

2040

6080

pres

tige

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Smoothing and correlation (Currie and Durban, 2002)

AIC and GCV lead to underestimation of the smoothing parameter in thepresence of positive serial correlation. The general approach to modellingwith P-splines takes care of this problem.

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Smoothing and correlation (Currie and Durban, 2002)

AIC and GCV lead to underestimation of the smoothing parameter in thepresence of positive serial correlation. The general approach to modellingwith P-splines takes care of this problem.

Wood profile data

320 measurements of the profile of a block of wood subject to grinding.

Sampling distance

Pro

file

0 50 100 150 200 250 300

7080

9010

011

012

0

19

Lag

AC

F

0 5 10 15 20 25

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Residuals AR(1)

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0 50 100 150 200 250 300Sampling Distance

7080

9010

011

012

0

Pro

file

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Lag

AC

F

0 5 10 15 20 25

0.0

0.2

0.4

0.6

0.8

1.0

Residuals AR(2)

Other examples in Durban and Currie (2003), Computational Statistics.

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Smoothing and heteroscedasticity (Currie and Durban(2002)

Simulated experiment to test crash helmets, 133 head accelerations andtimes after impact

Time (ms)

Acc

eler

atio

n (g

)

10 20 30 40 50

-100

-50

050

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Fit y = Ba + ε with V ar(ε) = σ2V and V = W−1,W = diag(w1, . . . , wn).

Use P-splines to smooth Ri = log r2i r2

i = (yi − yi)2/σ2 andw−1

i ∝ exp(Ri).

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Time (ms)

Res

idua

ls s

quar

ed

10 20 30 40 50

02

46

810

••••• •••••••••••••••••••••••••••••

••••••

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Time (ms)

Inve

rse

wei

ghts

10 20 30 40 500

12

3

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Generalised additive models: Count data

The one-parameter exponential family model, with canonical link, has jointdensity,

f(y|η) = exp {y′η − 1′b(η) + 1′c(y)}the linear predictor η = Ba, using the mixed model representation ofP -splines we rewrite Ba = Xβ + Zu

f(y|u) = exp {y′(Xβ + Zu)− 1′exp(Xβ + Zu)− 1′log(Γ(y + 1))}

and u ∼ N(0, σ2uI).

Iterate between penalised quasi-likelihood (PQL) of Breslow (1993) (toestimate β and u) and REML (to estimate variance components).

In the case of count data λ = 1/σ2u.

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The data

Male policyholders, source: Continuous Mortality Investigation Bureau(CMIB).

For each calendar year (1947-1999) and each age (11-100) we have:

• Number of years lived (the exposure).

• Number of policy claims (deaths).

Mortality of male policyholders has improved rapidly over the last 30 years

⇓Model mortality trends overtime and dependence on age.

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Additive model: Fitted curves for Ages 34 and 60

Year

log(

mu)

1950 1960 1970 1980 1990 2000

-7.8

-7.6

-7.4

-7.2

-7.0

-6.8

-6.6

-6.4

Year

log(

mu)

1950 1960 1970 1980 1990 2000-5

.0-4

.8-4

.6-4

.4-4

.2

Age: 34 Age: 60

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Tensor model: Fitted curves for Ages 34 and 60

Year

log(

mu)

1950 1960 1970 1980 1990 2000

-7.6

-7.4

-7.2

-7.0

-6.8

-6.6

-6.4

Year

log(

mu)

1950 1960 1970 1980 1990 2000

-5.0

-4.8

-4.6

-4.4

-4.2

Age: 34 Age: 60

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Age-Period

Age-Period-Cohort

Tensor

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Forecasting with P-splines

Treat the forecasting of future values as a missing value problem.

• We have data for ny years and na ages and wish to forecast nf years

• Define a weight matrix V = blockdiagonal(I,0) I is an identity matrixof size nyna, 0 is a square matrix of size nf

• Define a new basis: B = BV and proceed as before

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Forecast

Age: 34

1950 1960 1970 1980 1990 2000

Year

-8.5

-8.0

-7.5

-7.0

-6.5

log(

mu)

Age: 60

1950 1960 1970 1980 1990 2000

Year

-5.5

-5.0

-4.5

-4.0

log(

mu)

TruePredictionC.I.

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P-splines for longitudinal data

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The data

Objetive: Determine the effect of 4 surgical treatments on coronary sinuspotasium in dogs

• 36 dogs

• 4 treatments

• 7 measurements per dog

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2 4 6 8 10 12

time

3.0

3.5

4.0

4.5

5.0

5.5

6.0

pota

ssiu

m

Group 1

2 4 6 8 10 12

time

3.0

3.5

4.0

4.5

5.0

5.5

pota

ssiu

m

Group 2

2 4 6 8 10 12

time

3.0

3.5

4.0

4.5

5.0

5.5

6.0

pota

ssiu

m

Group 3

2 4 6 8 10 12

time

3.0

3.5

4.0

4.5

5.0

5.5

pota

ssiu

m

Group 4

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Models for longitudinal data

Basic Model yij = α0 + α1tij + βi0 + εij 1 ≤ j ≤ 7 1 ≤ i ≤ 36

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⇓ Relax linearity assumption

Model A yij = fgr(i)(tij) + βi0 + εij 1 ≤ gr(i) ≤ 4

35





⇓ Add random slope + general covariance matrix

Model B yij = fgr(i)(tij) + βi0 + βi1tij + εij

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⇓ Add random slope + general covariance matrix

Model B yij = fgr(i)(tij) + βi0 + βi1tij + εij

⇓ Subject specific curves

Model C yij = fgr(j)(tij) + gi(tij) + εij

36The mixed model associated to Model A is:

y = X + Zu + ε Cov

[u

ε

]=

ΣgrI 0 0

0 σ2β0

0

0 0 σ2I

X =

X time...

X time

X time =

1 t1... ...

1 t7

Z =

Z1 1 0 · · · 0... ... . . . ...

1 0 · · · 0

Z2 0 1 · · · 0... ... . . . ...

0 1 · · · 0

Z3... ... ... ...

0 0 · · · 1... ... . . . ...

Z4 0 0 · · · 1

Zgr(i) =

Ztime...

Ztime

Σgr =

σ2

1I

σ22I

σ23I

σ24I

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time

pota

sium

2 4 6 8 10 12

4.0

4.4

4.8

5.2

time

pota

sium

2 4 6 8 10 12

3.4

3.5

3.6

3.7

time

pota

sium

2 4 6 8 10 12

3.4

3.8

4.2

4.6

time

pota

sium

2 4 6 8 10 12

3.6

3.8

4.0

4.2

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The mixed model associated to Model B is:

y = X + Zu + ε Cov

[u

ε

]=

Σgr 0 0

0 blockdiag(Σ) 0

0 0 σ2I

Z =

Z1 X time 0 · · · 0... ... . . . ...

X time 0 · · · 0Z2 0 X time · · · 0

... ... . . . ...0 X time · · · 0

Z3... ... ... ...0 0 · · · X time... ... . . . ...

Z4 0 0 · · · X time

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The mixed model associated to Model C is:

y = X + Zu + ε Cov

[u

ε

]=

Σgr 0 0 0

0 blockdiag(Σ) 0 0

0 0 σ2cI 0

0 0 0 σ2I

Z =

Z1 X time 0 · · · 0 Ztime 0 · · · 0... ... . . . ... ... ... . . . ...

X time 0 · · · 0 Ztime 0 · · · 0

Z2 0 X time · · · 0 0 Ztime · · · 0... ... . . . ... ... ... . . . ...

0 X time · · · 0 0 Ztime · · · 0

Z3... ... ... ... ... ... ... ...

0 0 · · · X time 0 0 · · · Ztime... ... . . . ... ... ... . . . ...

Z4 0 0 · · · X time 0 0 · · · Ztime

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time

pota

sium

2 4 6 8 10 12

2.8

3.0

3.2

3.4

3.6

3.8

4.0

4.2

time

pota

sium

2 4 6 8 10 12

4.5

5.0

5.5

time

pota

sium

2 4 6 8 10 12

3.2

3.4

3.6

3.8

time

pota

sium

2 4 6 8 10 12

4.2

4.4

4.6

4.8

time

pota

sium

2 4 6 8 10 12

2.8

3.0

3.2

3.4

3.6

time

pota

sium

2 4 6 8 10 12

3.5

4.0

4.5

5.0

time

pota

sium

2 4 6 8 10 12

3.4

3.6

3.8

4.0

4.2

time

pota

sium

2 4 6 8 10 12

3.5

4.0

4.5

5.0

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Conclusions and work in progress

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Conclusions and work in progress

• P -splines are useful tool to model data in many situations

• P-splines as mixed models

• Easy to implement in standard sorfware

• Model selection

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References

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References

• Currie, I. and Durban, M. and Eilers, P. (2003). Smoothing and forecasting mortality

rates.

• Currie, I. and Durban, M. (2002). Flexible smoothing with P-splines: a unified

approach. Statistical Modelling 2.

• Durban, M. and Currie,I. (2003). A note on P -Spline additive models with correlated

errors. Computational Statistics, 18.

• Durban, M., Harezla,J., Carrol, R. and Wand, M. (2003). Simple fitting of

subject-specific curves for longitudinal data.

• Eilers, P.H.C. & Marx, B.D. (1996). Flexible smoothing with B-splines ans penalties.

Statist. Sci. 11.

• Ruppert, D., Wand, M.P., Carroll, R.J. (2003). Semiparametric Regression. Cambridge

University Press.

• Wand, M.P. (2003). Smoothing and mixed models. Comput. Stat. 18.

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