1. Michael A. Burr, Eynat Rafalin, and Diane L. Souvaine
Simplicial Depth: An Improved Definition, Analysis, and Efficiency in the Finite Sample Case
Michael A. Burr, Eynat Rafalin, and Diane L. Souvaine
Tufts University
Should be all set – colors on this page?
CCCG 2004
NSF grant #EIA

2. Introduction Introduction to Data Depth Simplicial Depth
Why?
Examples
Desirable Properties
Simplicial Depth
Definition
Properties
Problems
Revised Definition
Ongoing work
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3. What is Data Depth and Why?
Measures how deep (central) a given point is relative to a distribution or a data cloud.
Deals with the shape of the data.
Can be thought of as a measure of how well a point characterizes a data set
Provides an alternative to classical statistical analysis.
No assumption about the underlying distribution of the data.
Deals with outliers.
Why study?
Many measures are geometric in nature.
Can be computationally expensive to compute depth.
Should be all set – Difficult to talk about outliers with this data set but the contours with the other data set are bad.

4. Examples Half-Space (Tukey, Location) (Tukey 75)
Regression Depth (Rousseeuw and Hubert 94)
Simplicial Depth (Liu 90)
… and many more.
2 Data Points in this Half-plane
3 Data Points in this Half-plane
Should be all set - Check the year on Regression depth. Which side should be gray?

5. Desirable Properties of Data Depth
Liu (90) / Serfling and Zuo (00)
P1 – Affine Invariance
P2 – Maximality at Center
P3 – Monotonicity Relative to Deepest Point
P4 – Vanishing at Infinity
We propose (BRS 04)
P5 – Invariance Under Dimensions Change
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6. Affine Invariance (P1) A – affine transformation
Should be all set – more information about this specific affine transformation?

7. Maximality at Center (P2)
p is the center
q is any point
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8. Monotonicity Relative to Deepest Point (P3)
point between p and q
p is the deepest point
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q is any point

9. Vanishing at Infinity (P4)
q is far from the data cloud
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10. Invariance Under Dimensions Change (P5)
Is this an data set?
Is this an data set?
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11. Simplicial Depth (Liu 90)
The simplicial depth of a point p with respect to a probability distribution F in is the probability that a random closed simplex in contains p.
where is a closed simplex formed by d+1 random observations from F.
The simplicial depth of a point p with respect to a data set in is the fraction of closed simplicies formed by d+1 points of S containing p.
where I is the indicator function.
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12. Sample Version of Simplicial Depth
The simplicial depth of a point p with respect to a data set in is the fraction of closed simplicies formed by d+1 points of S containing p.
Total number of simplicies= =20
( ) 6 3 .2 .3 .3 .2
p is contained in 6 simplicies .3 .3 .4 .4
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The depth of p= =.3 6 20 __ .3 .4 .4 .2 .4 .2 .3 .3 .3 .3 .2

13. Properties
Is a statistical depth function in the continuous case. (Liu 90)
Is affine invariant (P1) and vanishes at infinity (P4) in the sample case. (Serfling and Zuo 00)
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14. Problems in the Sample Case
Does not always attain maximality at the center (P2) and does not always have monotonicity relative to the deepest point (P3). (Serfling and Zuo 00)
The depth on the boundary of cells is at least the depth in each of the adjacent cells – causes discontinuities.
Does not have invariance under dimensions change (P5).
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15. Simplicial Depth (Liu 90)
(BRS 04) .6 .3 B C .6 .3 .3 .4 .4 E .5 .3 .3 Y .8 .5 .4 .4 .7 .4 X .5
.35 .6 .3 .3 D
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Averaging number of closed and open simplicies containing a point .6 A
Total number of simplicies = ( ) = 10 5 3

16. Revised Definition (BRS 04)
The simplicial depth of a point p with respect to a data set in is the average of the fraction of closed simplicies containing p and the fraction of open simplicies containing p, formed by d+1 points of S.
Equivalently
- the fraction of simplicies with data points as vertices which contain p in their open interior.
- the fraction of simplicies with data points as vertices which contain p in their boundary.
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17. Properties of the Revised Definition
Reduces to the original definition, for continuous distributions and for points lying in the interior of cells.
Keeps ranking order of data points
Can be calculated using the existing algorithms, with slight modifications.
Fixes Zuo and Serfling’s counterexamples.
The depth on the boundary of two cells is the average of the two adjacent cells.
Invariant under dimensions change (P5) for the change from to .
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18. Invariance Under Dimension Change (P5)
Degenerate simplicies
Both points C and A (a point between B and C) lie within the open (degenerate) simplex BCD – think of it as a very thin triangle.
Both points B and D are vertices of the (degenerate) simplex BCD.
For a point, p, consider the ratio:
For both definitions, the ratio for a position (non-data point) is 2/3.
For Liu’s definition, the ratio for a data point is not 2/3.
For the BRS definition, the ratio for a data point is 2/3.
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19. Remaining Problems (P2 and P3)
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20. Remaining Problems (Data Points)
Data points are still over counted – there can still be discontinuities at data points. However, to fix the depth at data points, more features need to be considered.
Data points are inherently part of simplicies (a point makes a triangle with every other pair of points) and edges are inherently part of simplicies (the two endpoints of an edge make a triangle with every other vertex).
To retain invariance under dimensions change (P5), given a data set in , which lies on a d-flat, then the depth of a point when the data set is evaluated as a d-dimensional data set should be a multiple of the depth when the data set is evaluated as a b-dimensional data set.
Neither of the above ideas completely solve the problem and it appears that the best solutions take into account the geometry of the entire data set.
Should be all set – too many words? Check d-flat

21. Ongoing Work
The current algorithm for finding the median (the deepest point) is O(n4) to walk an arrangement of O(n2) segments.
We can improve this algorithm by comparing simplicial depth and half-space depth.
We are further improving this by considering simplicial depth in the dual.
The problems with data points are improved by generalizing this work to higher dimensions.
To find the depth at all points, we are using local information to form an approximation for the depth measure.
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22. References
G. Aloupis, C. Cortes, F. Gomez, M. Soss, and G. Toussaint. Lower bounds for computing statistical depth. Computational Statistics & Data Analysis, 40(2): , 2002.
G. Aloupis, S. Langerman, M. Soss, and G. Toussaint. Algorithms for bivariate medians and a Fermat-Torricelli problem for lines. In Proc. 13th CCCG, pages 21-24, 2001.
M. Burr, E. Rafalin, and D. L. Souvaine. Simplicial depth: An improved definition, analysis, and efficiency for the sample case. Technical report , DIMACS, 2003.
A. Y. Cheng and M. Ouyang. On algorithms for simplicial depth. In Proc. 13th CCCG, pages 53-56, 2001.
J. Gil, W. Steiger, and A. Wigderson. Geometric medians. Discrete Math., 108(1-3):37-51, Topological, algebraical and combinatorial structures. Frolík's memorial volume.
S. Khuller and J. S. B. Mitchell. On a triangle counting problem. Inform. Process. Lett., 33(6): , 1990.
R. Liu. On a notion of data depth based on random simplices. Ann. of Statist., 18: , 1990.
Y. Zuo and R. Serfling. General notions of statistical depth function. Ann. Statist., 28(2): , 2000.
Should be all set – too much?