nicola loperfido università degli studi di urbino carlo...

Nicola LoperfidoUniversità degli Studi di Urbino "Carlo Bo“,

Dipartimento di Economia, Società e PoliticaVia Saffi 42, Urbino (PU), ITALY

e-mail: [email protected]

Wirtschaftsuniversität Wien, 03-24-20171/42

Nicola Loperfido, Urbino University

OutlineFinite mixtures

Third moment

Multivariate skewness

Decathlon data

Nicola Loperfido, Urbino UniversityWirtschaftsuniversität Wien,

03-24-20172/42

Mixtures: problem

Are the data skewed because they come fromdifferent populations?

Nicola Loperfido, Urbino University3/42Wirtschaftsuniversität Wien,

03-24-2017

Mixtures: quotation"I have at present been unable to find any generalcondition among the moments, which would beimpossible for a skew curve and possible for acompound, and so indicate compoundness. I donot, however, despair of one being found".(Pearson, 1895)


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Mixtures: blood pressureBlood pressure data are skewed.

Platt (1963): hypertension is an illness present in agenetically defined subpopulation.

Pickering (1968): hypertension is a labeling forthose in the upper tail of the population.


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Mixtures: definition

A probability density function f(∙) is a finitemixture if it can be represented as a weightedaverage of several probability densityfunctions:

g

i

ii xfxf1


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Mixtures: special cases

Normal: each component is normally distributed.

Two-component: there are only two components.

Location: components only differ in location.


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Mixtures: options

Components

Densities

Parameters


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Mixtures: advantages

Flexibility

Interpretability

Tractability


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Mixtures: inferenceMaximum likelihood overestimates the number ofcomponents when they are erroneously assumedto be symmetric.

How can we choose between different models?


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Kullback-Leibler divergenceLet p and q be two probability density functions.The Kullback-Leibler divergence of p from q is

xdxq

xpxpqpJ ln,


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Kronecker product


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Third moment: definition


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Kullback-Leibler approximationLet p and q be two densities with identical means,identical covariances and possibly different thirdcumulants K3,p and K3,q. Then their Kullback-Lieblerdivergence is approximately ||K3,p -K3,q||2/12, where||.|| denotes the Euclidean distance (Lin et al, 1999).


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Third moment: standardization

The third standardized moment (cumulant) of x isthe third moment of z=-½(x-μ).


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TensorsA real tensor is a multidimensional array ofreal values identified by a vector ofsubscripts:


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Third-order tensors


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Tensor rankThe rank of a n1 n2 n3 third-order tensorA is the smallest number r satisfying


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Symmetric tensor rankA p²×p third moment M3,x has symmetric tensorrank k if there are k p-dimensional real vectors v1,..., vk satisfying

.111,3 k

T

kk

T

x vvvvvvM


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Third moment: special cases

The symmetric tensor rank of the third moment is

easily recovered when the underlying distribution

is either a shape mixture of skew-normals or a

location normal mixture.


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Mardia’s skewness: definition

x, y P E(x) = V(x) = x, y i.i.d.

b1,pM = E[(x- )T -1(y- )]3


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Mardia’s skewness: standardizationMardia’s skewness is the trace of the product of

the third standardized moment and its transpose


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Mardias skewness: application

The Kullback-Liebler divergence between asymmetric, standardized random vector andanother standardized random vector isapproximately proportional to the Mardia’sskewness of the latter.


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Vectorial skewness: definition

22

1

22

11

pd

p

T

V

ZZZ

ZZZ

zzz


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Vectorial skewness: standardization

p

T

zV IvecM ,3


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Vectorial skewness: clustering

Nicola Loperfido, Urbino University26/42

When data come from a mixture of two weakly symmetricdistributions with different means and proportionalcovariances, the projection of the standardized data onto thedirection of a vectorial skewness consistently estimates thebest linear discriminant projection.

Wirtschaftsuniversität Wien, 03-24-2017

Directional skewness: definition


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Directional skewness: standardization

Directional skewness is a function

of the third standardized moment


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Directional skewness: fit Let be the total and directionalskewness of the p-dimensional density q. Then thesmallest Kullback-Liebler divergence of q from alocation mixture of two symmetric densities is

approximately .

⇓The fit to the data X of a location mixture of two symmetric densities might be assessed by the difference .

and ,2,2 qq D

p

M

p bb

- ,2,2 qq D

p

M

p bb

- ,1,2 XbXb D

p

M

p


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Decathlon data: description Units. The 23 athletes who scored points in all 10

events of the Olympic decathlon in Rio 2016.

Variables. Performances in each event, convertedinto decathlon points using IAAF scoring tables.

Source. The official website of the InternationalAssociation of Athletics Federations (IAAF).


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Decathlon data: original variables


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Decathlon data: summaries Data are slightly nonnormal, with low to moderate

levels of skewness and kurtosis.

The multiple scatterplot does not show anyparticular features, as for example outliers.

The number of variables is quite large with respectto the number of units.


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Decathlon data: principal components


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Decathlon data: summary PCA The first two principal component account for

about 55% of the total variation.

Their joint distribution is approximately normal.

They do not clearly suggest the presence ofoutliers.


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Decathlon data: Skewed components

We computed the two most skewed and mutuallyorthogonal projections of the Decathlon datausing the R package MaxSkew.


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Decathlon data: skewed components


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Decathlon data: outliersKarl Robert Saluri (EST). He scored lowest

due to lower-than-average performances innearly all events.

Jeremi Taiwo (USA). He obtained an aboutaverage score due to a very unusual patternof performances.


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Decathlon data : conclusion

The scatterplot of the first two most skewedcomponents clearly hints for the presence of twooutliers.


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Skewness &

Mixtures

Finite Mixtures

Definition

Properties

Problem

Third MomentsDefinition

Approximation

Tensor

Skewness Measures

Total

Vectorial

Directional

Decathlon dataOriginal variables:

no outliers

Principal components: one outliers

Skewed components: two outliers

39/42Wirtschaftsuniversität Wien, 03-24-2017Nicola Loperfido, Urbino University

Future research

Package Multiskew

Fourth moment

Tensor approach


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Essential references

Loperfido, N. (2015). Singular value decomposition of the thirdmultivariate moment. Lin. Alg. Appl. 473, 202-216.

Malkovich, J.F. and Afifi, A.A.(1973). On tests formultivariate normality. J. Amer. Statist. Ass. 68, 176-179.

Mardia, K.V. (1970). Measures of multivariate skewness andkurtosis with applications. Biometrika 57, 519-530.

• McLachlan, G. and Peel, D. (2000). Finite Mixture Models. JohnWiley and Sons Inc, New York.

Mori T.F., Rohatgi V.K. and Székely G.J. (1993). On multivariateskewness and kurtosis. Theory Probab. Appl. 38, 547-551.


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Thank you for your attention


03-24-2017

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