167 research outputs found

### A new metric between distributions of point processes

Most metrics between finite point measures currently used in the literature
have the flaw that they do not treat differing total masses in an adequate
manner for applications. This paper introduces a new metric $\bar{d}_1$ that
combines positional differences of points under a closest match with the
relative difference in total mass in a way that fixes this flaw. A
comprehensive collection of theoretical results about $\bar{d}_1$ and its
induced Wasserstein metric $\bar{d}_2$ for point process distributions are
given, including examples of useful $\bar{d}_1$-Lipschitz continuous functions,
$\bar{d}_2$ upper bounds for Poisson process approximation, and $\bar{d}_2$
upper and lower bounds between distributions of point processes of i.i.d.
points. Furthermore, we present a statistical test for multiple point pattern
data that demonstrates the potential of $\bar{d}_1$ in applications.Comment: 20 pages, 2 figure

### Zero biasing and a discrete central limit theorem

We introduce a new family of distributions to approximate $\mathbb {P}(W\in
A)$ for $A\subset\{...,-2,-1,0,1,2,...\}$ and $W$ a sum of independent
integer-valued random variables $\xi_1$, $\xi_2$, $...,$ $\xi_n$ with finite
second moments, where, with large probability, $W$ is not concentrated on a
lattice of span greater than 1. The well-known Berry--Esseen theorem states
that, for $Z$ a normal random variable with mean $\mathbb {E}(W)$ and variance
$\operatorname {Var}(W)$, $\mathbb {P}(Z\in A)$ provides a good approximation
to $\mathbb {P}(W\in A)$ for $A$ of the form $(-\infty,x]$. However, for more
general $A$, such as the set of all even numbers, the normal approximation
becomes unsatisfactory and it is desirable to have an appropriate discrete,
nonnormal distribution which approximates $W$ in total variation, and a
discrete version of the Berry--Esseen theorem to bound the error. In this
paper, using the concept of zero biasing for discrete random variables (cf.
Goldstein and Reinert [J. Theoret. Probab. 18 (2005) 237--260]), we introduce a
new family of discrete distributions and provide a discrete version of the
Berry--Esseen theorem showing how members of the family approximate the
distribution of a sum $W$ of integer-valued variables in total variation.Comment: Published at http://dx.doi.org/10.1214/009117906000000250 in the
Annals of Probability (http://www.imstat.org/aop/) by the Institute of
Mathematical Statistics (http://www.imstat.org

### On approximation of Markov binomial distributions

For a Markov chain $\mathbf{X}=\{X_i,i=1,2,...,n\}$ with the state space
$\{0,1\}$, the random variable $S:=\sum_{i=1}^nX_i$ is said to follow a Markov
binomial distribution. The exact distribution of $S$, denoted $\mathcal{L}S$,
is very computationally intensive for large $n$ (see Gabriel [Biometrika 46
(1959) 454--460] and Bhat and Lal [Adv. in Appl. Probab. 20 (1988) 677--680])
and this paper concerns suitable approximate distributions for $\mathcal{L}S$
when $\mathbf{X}$ is stationary. We conclude that the negative binomial and
binomial distributions are appropriate approximations for $\mathcal{L}S$ when
$\operatorname {Var}S$ is greater than and less than $\mathbb{E}S$,
respectively. Also, due to the unique structure of the distribution, we are
able to derive explicit error estimates for these approximations.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ194 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

### Divergence from, and Convergence to, Uniformity of Probability Density Quantiles

The probability density quantile (pdQ) carries essential information
regarding shape and tail behavior of a location-scale family. Convergence of
repeated applications of the pdQ mapping to the uniform distribution is
investigated and new fixed point theorems are established. The Kullback-Leibler
divergences from uniformity of these pdQs are mapped and found to be
ingredients in power functions of optimal tests for uniformity against
alternative shapes.Comment: 13 pages, 2 figures. arXiv admin note: substantial text overlap with
arXiv:1605.0018

### Poisson process approximation: From Palm theory to Stein's method

This exposition explains the basic ideas of Stein's method for Poisson random
variable approximation and Poisson process approximation from the point of view
of the immigration-death process and Palm theory. The latter approach also
enables us to define local dependence of point processes [Chen and Xia (2004)]
and use it to study Poisson process approximation for locally dependent point
processes and for dependent superposition of point processes.Comment: Published at http://dx.doi.org/10.1214/074921706000001076 in the IMS
Lecture Notes Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org

### Stein's method, Palm theory and Poisson process approximation

The framework of Stein's method for Poisson process approximation is
presented from the point of view of Palm theory, which is used to construct
Stein identities and define local dependence. A general result (Theorem
\refimportantproposition) in Poisson process approximation is proved by taking
the local approach.
It is obtained without reference to any particular metric, thereby allowing
wider applicability. A Wasserstein pseudometric is introduced for measuring the
accuracy of point process approximation. The pseudometric provides a
generalization of many metrics used so far, including the total variation
distance for random variables and the Wasserstein metric for processes as in
Barbour and Brown [Stochastic Process. Appl. 43 (1992) 9-31]. Also, through the
pseudometric, approximation for certain point processes on a given carrier
space is carried out by lifting it to one on a larger space, extending an idea
of Arratia, Goldstein and Gordon [Statist. Sci. 5 (1990)
403-434]. The error bound in the general result is similar in form to that
for Poisson approximation. As it yields the Stein factor 1/\lambda as in
Poisson approximation, it provides good approximation, particularly in cases
where \lambda is large. The general result is applied to a number of problems
including Poisson process modeling of rare words in a DNA sequence.Comment: Published by the Institute of Mathematical Statistics
(http://www.imstat.org) in the Annals of Probability
(http://www.imstat.org/aop/) at http://dx.doi.org/10.1214/00911790400000002

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