47,870 research outputs found
On Some Distributions Arising from Certain Generalized Sampling Schemes
With the notion of success in a series of trials extended to refer to a run of like outcomes, several new distributions are obtained as the result of sampling from an urn without replacement or with additional replacements. In this context, the hypergeometric, negative hypergeometric, logarithmic series, generalized Waring, Polya and inverse Polya distributions are extended and their properties are studieddistribution of order k, hypergeometric, negative hypergeometric, logarithmic series, generalized Waring distribution, binomial, Poisson, negative binomial, Polya and inverse Polya distribution
On the Sum of Order Statistics and Applications to Wireless Communication Systems Performances
We consider the problem of evaluating the cumulative distribution function
(CDF) of the sum of order statistics, which serves to compute outage
probability (OP) values at the output of generalized selection combining
receivers. Generally, closed-form expressions of the CDF of the sum of order
statistics are unavailable for many practical distributions. Moreover, the
naive Monte Carlo (MC) method requires a substantial computational effort when
the probability of interest is sufficiently small. In the region of small OP
values, we propose instead two effective variance reduction techniques that
yield a reliable estimate of the CDF with small computing cost. The first
estimator, which can be viewed as an importance sampling estimator, has bounded
relative error under a certain assumption that is shown to hold for most of the
challenging distributions. An improvement of this estimator is then proposed
for the Pareto and the Weibull cases. The second is a conditional MC estimator
that achieves the bounded relative error property for the Generalized Gamma
case and the logarithmic efficiency in the Log-normal case. Finally, the
efficiency of these estimators is compared via various numerical experiments
Time-causal and time-recursive spatio-temporal receptive fields
We present an improved model and theory for time-causal and time-recursive
spatio-temporal receptive fields, based on a combination of Gaussian receptive
fields over the spatial domain and first-order integrators or equivalently
truncated exponential filters coupled in cascade over the temporal domain.
Compared to previous spatio-temporal scale-space formulations in terms of
non-enhancement of local extrema or scale invariance, these receptive fields
are based on different scale-space axiomatics over time by ensuring
non-creation of new local extrema or zero-crossings with increasing temporal
scale. Specifically, extensions are presented about (i) parameterizing the
intermediate temporal scale levels, (ii) analysing the resulting temporal
dynamics, (iii) transferring the theory to a discrete implementation, (iv)
computing scale-normalized spatio-temporal derivative expressions for
spatio-temporal feature detection and (v) computational modelling of receptive
fields in the lateral geniculate nucleus (LGN) and the primary visual cortex
(V1) in biological vision.
We show that by distributing the intermediate temporal scale levels according
to a logarithmic distribution, we obtain much faster temporal response
properties (shorter temporal delays) compared to a uniform distribution.
Specifically, these kernels converge very rapidly to a limit kernel possessing
true self-similar scale-invariant properties over temporal scales, thereby
allowing for true scale invariance over variations in the temporal scale,
although the underlying temporal scale-space representation is based on a
discretized temporal scale parameter.
We show how scale-normalized temporal derivatives can be defined for these
time-causal scale-space kernels and how the composed theory can be used for
computing basic types of scale-normalized spatio-temporal derivative
expressions in a computationally efficient manner.Comment: 39 pages, 12 figures, 5 tables in Journal of Mathematical Imaging and
Vision, published online Dec 201
Multifractal model of asset returns with leverage effect
Multifractal processes are a relatively new tool of stock market analysis.
Their power lies in the ability to take multiple orders of autocorrelations
into account explicitly. In the first part of the paper we discuss the
framework of the Lux model and refine the underlying phenomenological picture.
We also give a procedure of fitting all parameters to empirical data. We
present a new approach to account for the effective length of power-law memory
in volatility. The second part of the paper deals with the consequences of
asymmetry in returns. We incorporate two related stylized facts, skewness and
leverage autocorrelations into the model. Then from Monte Carlo measurements we
show, that this asymmetry significantly increases the mean squared error of
volatility forecasts. Based on a filtering method we give evidence on similar
behavior in empirical data.Comment: 23 pages, 8 figures, updated some figures and references, fixed two
typos, accepted to Physica
Generalized Negative Binomial Processes and the Representation of Cluster Structures
The paper introduces the concept of a cluster structure to define a joint
distribution of the sample size and its exchangeable random partitions. The
cluster structure allows the probability distribution of the random partitions
of a subset of the sample to be dependent on the sample size, a feature not
presented in a partition structure. A generalized negative binomial process
count-mixture model is proposed to generate a cluster structure, where in the
prior the number of clusters is finite and Poisson distributed and the cluster
sizes follow a truncated negative binomial distribution. The number and sizes
of clusters can be controlled to exhibit distinct asymptotic behaviors. Unique
model properties are illustrated with example clustering results using a
generalized Polya urn sampling scheme. The paper provides new methods to
generate exchangeable random partitions and to control both the cluster-number
and cluster-size distributions.Comment: 30 pages, 8 figure
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