Nonparametric indices of dependence between components for inhomogeneous multivariate random measures and marked sets
[.5in]
M.N.M. van Lieshout
[.2in] CWI, P.O. Box 94079, NL1090 GB Amsterdam
University of Twente, P.O. Box 217, NL7500 AE Enschede
The Netherlands
[.2in] In memory of J. Oosterhoff.
Abstract
We propose new summary statistics to quantify the association between the components in coveragereweighted moment stationary multivariate random sets and measures. They are defined in terms of the coveragereweighted cumulant densities and extend classic functional statistics for stationary random closed sets. We study the relations between these statistics and evaluate them explicitly for a range of models. Unbiased estimators are given for all statistics and applied to simulated examples.
Keywords & Phrases: compound random measure, coverage measure, coveragereweighted moment stationarity, cross hitting functional, empty space function, germgrain model, function, function, moment measure, multivariate random measure, random field model, reduced cross correlation measure, spherical contact distribution.
2010 Mathematics Subject Classification: 60D05.
1 Introduction
Popular statistics for investigating the dependencies between different types of points in a multivariate point process include crossversions of the function [32], the nearestneighbour distance distribution [11] or the function [22]. Although originally proposed under the assumption that the underlying point process distribution is invariant under translations, in recent years all statistics mentioned have been adapted to an inhomogeneous context. More specifically, for univariate point processes, [4] proposed an inhomogeneous extension of the function, whilst [21] did so for the nearestneighbour distance distribution and the function. An inhomogeneous cross function was proposed in [28], cross nearestneighbour distance distributions and functions were introduced in [21] and further studied in [8].
Although point processes can be seen as the special class of random measures that take integer values, functional summary statistics for random measures in general do not seem to be well studied. An exception is the pioneering paper by Stoyan and Ohser [34] in which, under the assumption of stationarity, two types of characteristics were proposed for describing the correlations between the components of a multivariate random closed sets in terms of their coverage measures. The first one is based on the second order moment measure [10] of the coverage measure [27], the second one on the capacity functional [25]. The authors did not pursue any relations between their statistics. Our goal in this paper is, in the context of multivariate random measures, to define generalisations of the statistics of [34] that allow for inhomogeneity, and to investigate the relations between them.
The paper is organised as follows. In Section 2 we review the theory of multivariate random measures. We recall the definition of the Laplace functional and Palm distribution and discuss the moment problem. We then present the notion of coveragereweighted moment stationarity. In Section 3 we introduce new inhomogeneous counterparts to Stoyan and Ohser’s reduced cross correlation measure. In the univariate case, the latter coincides with that proposed by Gallego et al. for germgrain models [14]. We go on to propose a cross statistic and relate it to the cross hitting intensity [34] and empty space function [25] defined for stationary random closed sets. Next, we give explicit expressions for our functional statistics for a range of bivariate models: compound random measures including linked and balanced models, the coverage measure associated to random closed sets such as germgrain models, and random field models with particular attention to logGaussian and thinning random fields. Then, in Section 5, we turn to estimators for the new statistics and apply them to simulations of the models discussed in Section 4.
2 Random measures and their moments
Definition 1.
Let , , be equipped with the metric defined by for and . Then a multivariate random measure on is a measurable mapping from a probability space into the space of all locally finite Borel measures on equipped with the smallest algebra that makes all with ranging through the bounded Borel sets and through a random variable.
An important functional associated with a multivariate random measure is its Laplace functional.
Definition 2.
Let be a multivariate random measure. Let be a bounded nonnegative measurable function such that the projections , , have bounded support. Then
is the Laplace functional of evaluated at .
The Laplace functional completely determines the distribution of the random measure [10, Section 9.4] and is closely related to the moment measures. First, consider the case . Then, for Borel sets and , set
Provided the set function is finite for bounded Borel sets, it yields a locally finite Borel measure that is also denoted by and referred to as the first order moment measure of . More generally, for , the th order moment measure is defined by the set function
where are Borel sets and . If is finite for bounded , it can be extended uniquely to a locally finite Borel measure on , cf. [10, Section 9.5].
In the sequel we shall need the following relation between the Laplace functional and the moment measures. Let be a bounded nonnegative measurable function such that its projections have bounded support. Then,
(1) 
provided that the moment measures of all orders exist and that the series on the right is absolutely convergent [9, (6.1.9)].
The above discussion might lead us to expect that the moment measures determine the distribution of a random measure. As for a random variable, such a claim cannot be made in complete generality. However, Zessin [37] derived a sufficient condition.
Theorem 1.
Let be a multivariate random measure and assume that the series
diverges for all bounded Borel sets and all . Then the distribution of is uniquely determined by its moment measures.
The existence of the firstorder moment measure implies that of a Palm distribution [10, Prop. 13.1.IV].
Definition 3.
Let be a multivariate random measure for which exists as a locally finite measure. Then admits a Palm distribution which is defined uniquely up to a nullset and satisfies
(2) 
for any nonnegative measurable function . Here, denotes expectation with respect to .
The equation (2) is sometimes referred to as the Campbell–Mecke formula.
Next, we will focus on random measures whose moment measures are absolutely continuous. Thus, suppose that
or, in other words, that is absolutely continuous with Radon–Nikodym derivative , the point coverage function. The family of s define cumulant densities as follows [10].
Definition 4.
Let be a multivariate random measure and assume that its moment measures exist and are absolutely continuous. Assume that the coverage function is strictly positive. Then the coveragereweighted cumulant densities are defined recursively by and, for ,
where the sum is over all possible partitions , , of . Here we use the labels to define which of the components is considered and denote the cardinality of by .
For the special case ,
Consequently, can be interpreted as a coveragereweighted covariance function.
Definition 5.
Let be a multivariate random measure. Then is coveragereweighted moment stationary if its coverage function exists and is bounded away from zero, , and its coveragereweighted cumulant densities exist and are translation invariant in the sense that
for all , and almost all .
An application of [9, Lemma 5.2.VI] to (1) implies that
(3)  
provided the series is absolutely convergent.
The next result states that the Palm moment measures of the coveragereweighted random measure can be expressed in terms of those of .
Theorem 2.
Let be a coveragereweighted moment stationary multivariate random measure and . Then for all bounded Borel sets and all , the Palm expectation
for almost all .
Proof.
By (2) with if and
for some bounded Borel sets and any , one sees that
The left hand side is equal to
and the inner integrand does not depend on the choice of by the assumptions on . Hence, for all bounded Borel sets ,
Therefore the Palm expectation takes the same value for almost all as claimed. ∎
3 Summary statistics for multivariate random measures
3.1 The inhomogeneous cross function
For the coverage measures associated to a stationary bivariate random closed set, Stoyan and Ohser [34] defined the reduced cross correlation measure as follows. Let be the closed ball of radius centred at and set, for any bounded Borel set of positive volume ,
(4) 
Due to the assumed stationarity, the definition does not depend on the choice of . In the univariate case, Ayala and Simó [2] called a function of this type the function in analogy to a similar statistic for point processes [11, 31].
In order to modify (4) so that it applies to more general, not necessarily stationary, random measures, we focus on the second order coveragereweighted cumulant density and assume it is invariant under translations. If additionally is bounded away from zero, is second order coveragereweighted stationary.
Definition 6.
Let be a bivariate random measure which admits a second order coveragereweighted cumulant density that is invariant under translations and a coverage function that is bounded away from zero. Then, for , the cross function is defined by
Note that the cross function is symmetric in the components of , that is, . The next result gives an alternative expression in terms of the expected content of a ball under the Palm distribution of the coveragereweighted random measure.
Lemma 1.
Let be a second order coveragereweighted stationary bivariate random measure and write for the closed ball of radius around . Then
and the right hand side does not depend on the choice of .
Proof.
To interpret the statistic, recall that is equal to the coveragereweighted covariance. Thus, if and are independent,
the Lebesgue measure of . Larger values are due to positive correlation, smaller ones to negative correlation between and . Furthermore, if is stationary, Lemma 1 implies that
which, by the Campbell–Mecke equation (2), is equal to
Consequently, , the reduced cross correlation measure of [34].
3.2 Inhomogeneous cross function
The cross function is based on the second order coveragereweighted cumulant density. In this section, we propose a new statistic that encorporates the coveragereweighted cumulant densities of all orders.
Definition 7.
Let be a coveragereweighted moment stationary bivariate random measure. For and , set
and define the cross function by
for all for which the series is absolutely convergent.
Note that
The appeal of Definition 7 lies in the fact that its dependence on the cumulant densities and, furthermore, its relation to are immediately apparent. However, being an alternating series, is not convenient to handle in practice. The next theorem gives a simpler characterisation in terms of the Laplace transform.
Theorem 3.
Let be a coveragereweighted moment stationary bivariate random measure. Then, for and ,
(5) 
for , provided the series expansions of and are absolutely convergent. In particular, does not depend on the choice of origin .
Proof.
First, note that, by (3), does not depend on the choice of . Also, by Theorem 2 and the series expansion (1) of the Laplace transform for , provided the series is absolutely convergent,
where and for . By splitting the last expression into terms based on whether the sets contain the index (i.e. on whether includes ), under the convention that , we obtain
where
, and is the power set of . Finally, by noting that the expansion contains terms of the form multiplied by a scalar and basic combinatorial arguments, we conclude that
The right hand side does not depend on and is absolutely convergent as a product of absolutely convergent terms. Therefore, so is the series expansion for . ∎
Heuristically, the cross function compares expectations under the Palm distribution to those under the distribution of . If the components of are independent, conditioning on the first component placing mass at the origin does not affect the second component, so . A value larger than means that such conditioning tends to lead to a smaller content (typical for negative association); analogously, suggests positive association between the components of .
4 Examples
In this section we calculate the cross  and statistics for a range of wellknown models.
4.1 Compound random measures
Let be a random vector such that its components take values in and have finite, strictly positive expectation. Set
(6) 
for some locally finite Borel measure on that is absolutely continuous with density function . In other words,
Theorem 4.
The bivariate random measure (6) is coveragereweighted moment stationary and
Both statistics do not depend on . The cross function is equal to the weighted Laplace functional of evaluated in .
To see that both statistics capture a form of ‘dependence’ between the components of , note that the cross function exceeds if and only if and are positively correlated. For the cross function, recall that two random variables and are negatively quadrant dependent if Cov whenever are nondecreasing functions, positively quadrant dependent if Cov (provided the moments exist) [12, 18, 20]. Applied to our context, it follows that if and are positively quadrant dependent, whilst if and are negatively quadrant dependent.
Proof.
Since
the coverage function of is given by
so that the coveragereweighted cumulant densities of are translation invariant. The assumptions imply that is bounded away from zero. Hence, is coveragereweighted moment stationary.
Specialising to second order, one finds that
from which the expression for follows upon integration.
Let us consider two specific examples discussed in [11, Section 6.6].
Linked model
Let for some . Since, for ,
and are positively quadrant dependent [12, Theorem 4.4] and, a fortiori, positively correlated. Therefore and
Balanced model
Let be supported on the interval for some and set . Since, for such that ,
and are negatively quadrant dependent [18] and, a fortiori, negatively correlated. Therefore and
4.2 Coverage measure of random closed sets
Let be a bivariate random closed set. Then, by Robbins’ theorem [27, Theorem 4.21], the Lebesgue content
of is a random variable for every Borel set and every component , . Letting and vary, one obtains a bivariate random measure denoted by . Clearly, is locally finite.
Reversely, a bivariate random measure defines a bivariate random closed set by the supports
where is the closed ball around with radius and is the topological closure of the Borel set . In other words, if , then every ball that contains has strictly positive mass. By [27, Prop. 8.16], the supports are welldefined random closed sets whose joint distribution is uniquely determined by that of the random measures.
Indeed, Ayala et al. [3] proved the following result.
Theorem 5.
Let be a multivariate random closed set. Then the distribution of is recoverable from if and only if is distributed as the (random) support of .
From now on, assume that is stationary. Then the hitting intensity [34] is defined as
where is any bounded Borel set of positive volume and is the closed ball centred at with radius . The definition does not depend on the choice of . The hitting intensity is similar in spirit to another classic statistic, the empty space function [25] defined by
The related cross spherical contact distribution can be defined as
in analogy to the classical univariate definition [6]. Again, the definitions do not depend on the choice of due to the assumed stationarity.
In order to relate and to our statistic, we need the concept of ‘scaling’. Let be a scalar. Then the scaling of by results in where .
Theorem 6.
Let be a stationary bivariate random closed set with strictly positive volume fractions , . Then the associated random coverage measure is coveragereweighted moment stationary and the following hold.

The cross statistics are

Use a subscript to denote that the statistic is evaluated for the scaled random closed set and let be as in Theorem 3. Then
and, for ,
whenever .
In words, the scaling limit of the cross function compares the empty space function to the cross spherical contact distribution.
Proof.
First note that
which, by [27, (4.14)] is equal to
Here, and are Borel subsets of . Hence, admits moment measures of all orders and the probabilities define the coverage functions. By assumption is bounded away from zero,