robust scale and autocovariance estimation
TRANSCRIPT
Robust scale and autocovariance estimation
Garth Tarr, Samuel Muller and Neville Weber
School of Mathematics and Statistics
THE UNIVERSITY OF SYDNEY
EMS 2013
Scale Autocovariance Conclusion
Outline
Robust Scale Estimator, Pn
Covariance
Autocovariance
Long RangeDependence
Short RangeDependence
Inverse CovarianceMatrix Estimation
Covariance Matrix
Scale Autocovariance Conclusion
Outline
Robust Scale Estimator, Pn
Covariance
Autocovariance
Long RangeDependence
Short RangeDependence
Inverse CovarianceMatrix Estimation
Covariance Matrix
Scale Autocovariance Conclusion
Outline
The robust scale estimator Pn
Autocovariance estimation using Pn
Conclusion and key references
Scale Autocovariance Conclusion
History of scale efficiencies at the Gaussian (n = 20)R
elat
ive
Effi
cien
cy
Year
SD
1894
1.00
1931 1974
0.38
1993
0.67
2012
0.85
Scale Autocovariance Conclusion
History of scale efficiencies at the Gaussian (n = 20)R
elat
ive
Effi
cien
cy
Year
SD
1894
1.00
IQR
1931
MAD
1974
0.38
1993
0.67
2012
0.85
Scale Autocovariance Conclusion
History of scale efficiencies at the Gaussian (n = 20)R
elat
ive
Effi
cien
cy
Year
SD
1894
1.00
IQR
1931
MAD
1974
0.38
Qn
1993
0.67
2012
0.85
Scale Autocovariance Conclusion
History of scale efficiencies at the Gaussian (n = 20)R
elat
ive
Effi
cien
cy
Year
SD
1894
1.00
IQR
1931
MAD
1974
0.38
Qn
1993
0.67
Pn
2012
0.85
Scale Autocovariance Conclusion
Pairwise mean scale estimator: Pn
• Consider the U -statistic, based on the pairwise mean kernel,
Un(X) :=
(n
2
)−1∑i<j
Xi +Xj
2.
• Let H(t) = P ((Xi +Xj)/2 ≤ t) be the cdf of the kernelswith corresponding empirical distribution function,
Hn(t) :=
(n
2
)−1∑i<j
I{Xi +Xj
2≤ t}, for t ∈ R.
Definition (Interquartile range of pairwise means)
Pn = c[H−1n (0.75)−H−1
n (0.25)],
where c ≈ 1.048 is a correction factor to ensure Pn is consistentfor the standard deviation when the underlying observations areGaussian.
Scale Autocovariance Conclusion
Influence curve
• Hampel (1974) defines the influence curve for a functional Tat distribution F as
IC(x;T, F ) = limε↓0
T ((1− ε)F + εδx)− T (F )
ε
where δx has all its mass at x.• Serfling (1984) outlines the IC for GL-statistics.
Influence curve for Pn (Tarr, Muller and Weber, 2012)
Assuming that F has derivative f > 0 on [F−1(ε), F−1(1− ε)] forall ε > 0,
IC(x;P , F ) = c
[0.75− F (2H−1
F (0.75)− x)∫f(2H−1
F (0.75)− x)f(x) dx
−0.25− F (2H−1
F (0.25)− x)∫f(2H−1
F (0.25)− x)f(x) dx
].
Scale Autocovariance Conclusion
Influence curve
• Hampel (1974) defines the influence curve for a functional Tat distribution F as
IC(x;T, F ) = limε↓0
T ((1− ε)F + εδx)− T (F )
ε
where δx has all its mass at x.• Serfling (1984) outlines the IC for GL-statistics.
Influence curve for Pn (Tarr, Muller and Weber, 2012)
Assuming that F has derivative f > 0 on [F−1(ε), F−1(1− ε)] forall ε > 0,
IC(x;P , F ) = c
[0.75− F (2H−1
F (0.75)− x)∫f(2H−1
F (0.75)− x)f(x) dx
−0.25− F (2H−1
F (0.25)− x)∫f(2H−1
F (0.25)− x)f(x) dx
].
Scale Autocovariance Conclusion
Influence curves when F = Φ
−4 −2 0 2 4
−1
01
2
x
IC(x
;T,Φ
)
Pn
Scale Autocovariance Conclusion
Influence curves when F = Φ
−4 −2 0 2 4
−1
01
2
x
IC(x
;T,Φ
)
PnSD
Scale Autocovariance Conclusion
Influence curves when F = Φ
−4 −2 0 2 4
−1
01
2
x
IC(x
;T,Φ
)
Pn
MAD
SD
Scale Autocovariance Conclusion
Influence curves when F = Φ
−4 −2 0 2 4
−1
01
2
x
IC(x
;T,Φ
)
Pn
MAD
Qn
SD
Scale Autocovariance Conclusion
Asymptotic variance and relative efficiency
• Tarr, Muller and Weber (2012) show that when the underlyingobservations are independent, Pn is asymptotically normalwith variance, V , given by the expected square of theinfluence function.
• When the underlying data are independent Gaussian,
V (Pn,Φ) =
∫IC(x;P ,Φ)2 d Φ(x) = 0.579.
• This equates to an asymptotic efficiency of 0.86 as comparedwith 0.82 for Qn and 0.37 for the MAD.
! But how does it compare at heavier tailed distributions?
Scale Autocovariance Conclusion
Asymptotic variance and relative efficiency
• Tarr, Muller and Weber (2012) show that when the underlyingobservations are independent, Pn is asymptotically normalwith variance, V , given by the expected square of theinfluence function.
• When the underlying data are independent Gaussian,
V (Pn,Φ) =
∫IC(x;P ,Φ)2 d Φ(x) = 0.579.
• This equates to an asymptotic efficiency of 0.86 as comparedwith 0.82 for Qn and 0.37 for the MAD.
! But how does it compare at heavier tailed distributions?
Scale Autocovariance Conclusion
Asymptotic variance and relative efficiency
• Tarr, Muller and Weber (2012) show that when the underlyingobservations are independent, Pn is asymptotically normalwith variance, V , given by the expected square of theinfluence function.
• When the underlying data are independent Gaussian,
V (Pn,Φ) =
∫IC(x;P ,Φ)2 d Φ(x) = 0.579.
• This equates to an asymptotic efficiency of 0.86 as comparedwith 0.82 for Qn and 0.37 for the MAD.
! But how does it compare at heavier tailed distributions?
Scale Autocovariance Conclusion
Asymptotic relative efficiency when f = tν for ν ∈ [1, 10]
1 2 3 54 76 98 10
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Degrees of freedom
Asy
mp
toti
cre
lati
veeffi
cien
cy
Pn
Scale Autocovariance Conclusion
Asymptotic relative efficiency when f = tν for ν ∈ [1, 10]
1 2 3 54 76 98 10
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Degrees of freedom
Asy
mp
toti
cre
lati
veeffi
cien
cy
Pn
MAD
Scale Autocovariance Conclusion
Asymptotic relative efficiency when f = tν for ν ∈ [1, 10]
1 2 3 54 76 98 10
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Degrees of freedom
Asy
mp
toti
cre
lati
veeffi
cien
cy
Pn
MAD
Qn
Scale Autocovariance Conclusion
Asymptotic distribution under short range dependence
Assumption 1: Gaussian SRD
Let {Xi}i≥1 be a stationary mean-zero Gaussian process withautocovariances γ(h) = E(X1Xh+1) satisfying
∑h≥1 |γ(h)| <∞.
Result
Under Assumption 1, it can be shown that,
√n(Pn − σ) =
cΦ√n
n∑i=1
IC(Xi, P ,Φ) + op(1),
hence applying a limit theorem from Arcones (1994) we have√n(Pn − σ)
D−→ N (0, σ2),
where σ =√γ(0) and
σ2 = E(IC2(X1, P ,Φ)
)+ 2
∑k≥1
E (IC(X1, P ,Φ)IC(Xk+1, P ,Φ)) .
Scale Autocovariance Conclusion
Asymptotic distribution under long range dependence
Assumption 2: Gaussian LRD
Let {Xi}i≥1 be a stationary mean-zero Gaussian process withautocovariance sequence γ(h) = E(X1Xh+1) satisfyingγ(h) = h−DL(h), 0 < D < 1, where L is slowly varying at infinity.
Result
Under Assumption 2,
1. If D > 1/2,√n(Pn − σ)
D−→ N (0, σ2),
σ2 = E IC2(X1, P ,Φ) + 2∑k≥1
E IC(X1, P ,Φ)IC(Xk+1, P ,Φ).
!Note that this is the same as the SRD result, however theproof is somewhat more involved.
Scale Autocovariance Conclusion
Asymptotic distribution under long range dependence
Assumption 2: Gaussian LRD
Let {Xi}i≥1 be a stationary mean-zero Gaussian process withautocovariance sequence γ(h) = E(X1Xh+1) satisfyingγ(h) = h−DL(h), 0 < D < 1, where L is slowly varying at infinity.
Conjecture
Under Assumption 2,
2. If D < 1/2,
k(D)nDL(n)−1(Pn − σ)D−→ σ
2
(Z2,D(1)− Z2
1,D(1)),
where k(D) = B((1−D)/2, D) and B denotes the betafunction Z1,D is the standard fBm process and Z2,D is theRosenblatt process.
!This is consistent with results for other scale estimators,e.g. SD and Qn (Levy-Leduc et. al., 2011) and the IQR...
Scale Autocovariance Conclusion
Interquartile range result
Consider equivalent result for the interquartile range,
Tn(x) = cΦ
(F−1n (3/4)− F−1
n (1/4)).
Result
Under Assumption 2 with D < 1/2, Tn satisfies the following limittheorem as n→∞:
k(D)nDL(n)−1(Tn − σ)D−→ σ
2
(Z2,D(1)− Z2
1,D(1)).
• In establishing this result we used Wu’s (2005) Bahadurrepresentation for sample quantiles under LRD:
F−1n (p)− r =
p− Fn(r)
φ(r)+X2n
2
φ′(r)
φ(r)+O(nh(D)L1(n)).
!It appears to be non-trivial to extend this result to the pairwisemean empirical distribution function.
Scale Autocovariance Conclusion
Interquartile range result
Consider equivalent result for the interquartile range,
Tn(x) = cΦ
(F−1n (3/4)− F−1
n (1/4)).
Result
Under Assumption 2 with D < 1/2, Tn satisfies the following limittheorem as n→∞:
k(D)nDL(n)−1(Tn − σ)D−→ σ
2
(Z2,D(1)− Z2
1,D(1)).
• In establishing this result we used Wu’s (2005) Bahadurrepresentation for sample quantiles under LRD:
F−1n (p)− r =
p− Fn(r)
φ(r)+X2n
2
φ′(r)
φ(r)+O(nh(D)L1(n)).
!It appears to be non-trivial to extend this result to the pairwisemean empirical distribution function.
Scale Autocovariance Conclusion
Interquartile range result
Consider equivalent result for the interquartile range,
Tn(x) = cΦ
(F−1n (3/4)− F−1
n (1/4)).
Result
Under Assumption 2 with D < 1/2, Tn satisfies the following limittheorem as n→∞:
k(D)nDL(n)−1(Tn − σ)D−→ σ
2
(Z2,D(1)− Z2
1,D(1)).
• In establishing this result we used Wu’s (2005) Bahadurrepresentation for sample quantiles under LRD:
F−1n (p)− r =
p− Fn(r)
φ(r)+X2n
2
φ′(r)
φ(r)+O(nh(D)L1(n)).
!It appears to be non-trivial to extend this result to the pairwisemean empirical distribution function.
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
30
40
0.95 1.00 1.05
Scale estimate
Den
sity
Pn
87%SD Relative Efficiency
Qn 83%IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
30
40
0.95 1.00 1.05
Scale estimate
Den
sity
Pn 87%SD Relative Efficiency
Qn 83%IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
30
40
0.95 1.00 1.05
Scale estimate
Den
sity
Pn 87%SD Relative Efficiency
Qn 83%
IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
30
40
0.95 1.00 1.05
Scale estimate
Den
sity
Pn 87%SD Relative Efficiency
Qn 83%IQR 39%
MAD 39%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
30
40
0.95 1.00 1.05
Scale estimate
Den
sity
Pn 87%SD Relative Efficiency
Qn 83%IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn
94%SD Relative Efficiency
Qn 94%IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%
IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%IQR 82%
MAD 83%
Scale Autocovariance Conclusion
Empirical densities ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities with 2% contamination at 5
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Empirical densities with 2% contamination at 6
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Empirical densities with 2% contamination at 7
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Empirical densities with 2% contamination at 8
0
2
4
1.2 1.4 1.6 1.8 2.0
Scale estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Outline
The robust scale estimator Pn
Autocovariance estimation using Pn
Conclusion and key references
Scale Autocovariance Conclusion
From scale to autocovariance
• Gnanadesikan and Kettenring (1972) highlighted an identitythat relates scale and covariance.
• In the autocovariance setting,
γ(h) = cov(X1, Xh+1) =1
4[var(X1+Xh+1)− var(X1−Xh+1)] .
• For a series of n observations, Xn = {Xt}1≤t≤n,
γP (h) =1
4
[P 2n−h(X1:n−h+Xh+1:n)− P 2
n−h(X1:n−h−Xh+1:n)].
where X1:n−h are the first n− h observations in Xn andXh+1:n are the last n− h observations.
• The same technique can be used to turn other scaleestimators into covariance and correlation estimators, e.g. Maand Genton (2000) study γQ based on Qn.
Scale Autocovariance Conclusion
Robustness properties
• Following Genton and Ma (1999), we have shown thatinfluence curve, IC((x, y), γP ,Φ), and therefore the grosserror sensitivity of γP can be derived from the IC of Pn.
x−4 −2 0 2 4
y−4
−20
24
IC(x,y)
−3−2−10123
IC((x,y),Pn,Φ)
! IC is bounded and hence the gross error sensitivity is finite.
Scale Autocovariance Conclusion
Robustness properties
• Following Genton and Ma (1999), we have shown thatinfluence curve, IC((x, y), γP ,Φ), and therefore the grosserror sensitivity of γP can be derived from the IC of Pn.
x−4 −2 0 2 4
y−4
−20
24
IC(x,y)
−3−2−10123
IC((x,y),Pn,Φ)
! IC is bounded and hence the gross error sensitivity is finite.
Scale Autocovariance Conclusion
Robustness properties
• Following Genton and Ma (1999), we have shown thatinfluence curve, IC((x, y), γP ,Φ), and therefore the grosserror sensitivity of γP can be derived from the IC of Pn.
x−4 −2 0 2 4
y−4
−20
24
IC(x,y)
−3−2−10123
IC((x,y),Pn,Φ)
! IC is bounded and hence the gross error sensitivity is finite.
Scale Autocovariance Conclusion
Breakdown value
• Ma and Genton (2000) show that the breakdown value forautocovariance estimators is (roughly) half that of thecorresponding covariance estimator.
• Consider n = 13
, autocovariance at lag h = 2
• Working with X1:11 ±X3:13
• 4 contaminated observations, denoted by
• Leaves only 3 uncontaminated pairs, denoted by
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11X9 X10 X11 X12 X13
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X1 X2
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X11 X12 X13
! γP (h) has roughly half the 13.4% breakdown value of Pn.
Scale Autocovariance Conclusion
Breakdown value
• Ma and Genton (2000) show that the breakdown value forautocovariance estimators is (roughly) half that of thecorresponding covariance estimator.
• Consider n = 13, autocovariance at lag h = 2
• Working with X1:11 ±X3:13
• 4 contaminated observations, denoted by
• Leaves only 3 uncontaminated pairs, denoted by
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11X9 X10 X11
X12 X13
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X1 X2
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X11 X12 X13
! γP (h) has roughly half the 13.4% breakdown value of Pn.
Scale Autocovariance Conclusion
Breakdown value
• Ma and Genton (2000) show that the breakdown value forautocovariance estimators is (roughly) half that of thecorresponding covariance estimator.
• Consider n = 13, autocovariance at lag h = 2
• Working with X1:11 ±X3:13
• 4 contaminated observations, denoted by
• Leaves only 3 uncontaminated pairs, denoted by
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
X9 X10 X11
X12 X13
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
X1 X2
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
X11 X12 X13
! γP (h) has roughly half the 13.4% breakdown value of Pn.
Scale Autocovariance Conclusion
Breakdown value
• Ma and Genton (2000) show that the breakdown value forautocovariance estimators is (roughly) half that of thecorresponding covariance estimator.
• Consider n = 13, autocovariance at lag h = 2
• Working with X1:11 ±X3:13
• 4 contaminated observations, denoted by
• Leaves only 3 uncontaminated pairs, denoted by
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11X9 X10 X11 X12 X13
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
X1 X2
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X11 X12 X13
! γP (h) has roughly half the 13.4% breakdown value of Pn.
Scale Autocovariance Conclusion
Breakdown value
• Ma and Genton (2000) show that the breakdown value forautocovariance estimators is (roughly) half that of thecorresponding covariance estimator.
• Consider n = 13, autocovariance at lag h = 2
• Working with X1:11 ±X3:13
• 4 contaminated observations, denoted by
• Leaves only 3 uncontaminated pairs, denoted by
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11X9 X10 X11 X12 X13
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
X1 X2
h = 2
X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13X11 X12 X13
! γP (h) has roughly half the 13.4% breakdown value of Pn.
Scale Autocovariance Conclusion
Asymptotic distribution under short range dependence
Result
If {Xi}i≥1 satisfy Assumption 1 (SRD) then the sequences{Xi +Xi+h}i≥1 and {Xi −Xi+h}i≥1 also satisfy Assumption 1,hence we can use the previous results and apply the functionaldelta method to show,
√n(γP (h)− γ(h))
D−→ N (0, σ2(h)),
where
σ2(h) = E[IC2((X1, X1+h), γP ,Φ)
]+ 2
∑k≥1
E [IC((X1, X1+h), γP ,Φ) IC((Xk+1, Xk+1+h), γP ,Φ)] .
Scale Autocovariance Conclusion
Asymptotic distribution under long range dependence
Result
Under Assumption 2 (LRD),
1. D > 1/2 (same limiting distribution as SRD):
√n(γP (h)− γ(h))
D−→ N (0, σ2(h)).
2. D < 1/2 (based on conjectured result):
k(D)nD
L(n)(γP (h)−γ(h))
D−→ γ(0) + γ(h)
2(Z2,D(1)− Z2
1,D(1)),
where k(D) = Beta((1−D)/2, D), Z1,D is the standard fBmprocess, Z2,D is the Rosenblatt process and
L(n) = 2L(n)+L(n+h)(1+h/n)−D+L(n−h)(1−h/n)−D.
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
0.95 1.00 1.05 1.10First order autocovariance estimate
Den
sity
Pn
87%SD Relative Eff.
Qn 83%IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
0.95 1.00 1.05 1.10First order autocovariance estimate
Den
sity
Pn 87%SD Relative Eff.
Qn 83%IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
0.95 1.00 1.05 1.10First order autocovariance estimate
Den
sity
Pn 87%SD Relative Eff.
Qn 83%
IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
0.95 1.00 1.05 1.10First order autocovariance estimate
Den
sity
Pn 87%SD Relative Eff.
Qn 83%IQR 39%
MAD 39%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.1, 0); D = 0.8; n = 5000
0
10
20
0.95 1.00 1.05 1.10First order autocovariance estimate
Den
sity
Pn 87%SD Relative Eff.
Qn 83%IQR 39%MAD 39%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn
94%SD Relative Efficiency
Qn 94%IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%
IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%IQR 82%
MAD 83%
Scale Autocovariance Conclusion
Empirical densities γ•(1) ARFIMA(0, 0.45, 0); D = 0.1; n = 5000
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn 94%SD Relative Efficiency
Qn 94%IQR 82%MAD 83%
Scale Autocovariance Conclusion
Empirical densities γ•(1) with 2% contamination at 5
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Empirical densities γ•(1) with 2% contamination at 6
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Empirical densities γ•(1) with 2% contamination at 7
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Empirical densities γ•(1) with 2% contamination at 8
0.0
0.5
1.0
1.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5First order autocovariance estimate
Den
sity
Pn
SD
QnIQRMAD
Scale Autocovariance Conclusion
Outline
The robust scale estimator Pn
Autocovariance estimation using Pn
Conclusion and key references
Scale Autocovariance Conclusion
Summary
1. Aim
• Efficient and robust scale and autocovariance estimation.
2. Method
• Pn scale estimator transformed using the GK identity.
3. Results
• 86% asymptotic efficiency at the Gaussian and highasymptotic efficiency at heavier tailed distributions.
• Robustness properties transfer from Pn to γP .
• In certain LRD settings robust estimators have very highefficiencies relative to the standard deviation.
Scale Autocovariance Conclusion
Summary
1. Aim
• Efficient and robust scale and autocovariance estimation.
2. Method
• Pn scale estimator transformed using the GK identity.
3. Results
• 86% asymptotic efficiency at the Gaussian and highasymptotic efficiency at heavier tailed distributions.
• Robustness properties transfer from Pn to γP .
• In certain LRD settings robust estimators have very highefficiencies relative to the standard deviation.
Scale Autocovariance Conclusion
Summary
1. Aim
• Efficient and robust scale and autocovariance estimation.
2. Method
• Pn scale estimator transformed using the GK identity.
3. Results
• 86% asymptotic efficiency at the Gaussian and highasymptotic efficiency at heavier tailed distributions.
• Robustness properties transfer from Pn to γP .
• In certain LRD settings robust estimators have very highefficiencies relative to the standard deviation.
Scale Autocovariance Conclusion
References
Genton, M. and Ma, Y. (1999).
Robustness properties of dispersion estimators.
Statistics & Probability Letters, 44(4):343–350.
Gnanadesikan, R. and Kettenring J. R. (1972).
Robust estimates, residuals and outlier detection with multiresponse data
Biometrics, 28(1):81–124.
Hampel, F. (1974).
The influence curve and its role in robust estimation.
Journal of the American Statistical Association, 69(346):383–393.
Levy-Leduc, C., Boistard, H., Moulines, E., Taqqu, M.S. and Reisen, V.A.(2011).
Robust estimation of the scale and of the autocovariance function ofGaussian short- and long-range dependent processes
Journal of Time Series Analysis, 32(2):135–156.
Scale Autocovariance Conclusion
References
Ma, Y. and Genton, M. (2000).
Highly robust estimation of the autocovariance function
Journal of Time Series Analysis, 21(6):663–684.
Serfling, R. J. (1984).
Generalized L-, M -, and R-statistics.
The Annals of Statistics, 12(1):76–86.
Tarr, G., Muller, S. and Weber, N.C., (2012).
A robust scale estimator based on pairwise means.
Journal of Nonparametric Statistics, 24(1):187–199.
Wu, W.B., (2005).
On the Bahadur representation of sample quantiles for dependentsequences.
Annals of Statistics, 33(4):1934–1963.