Search

In this article, a heuristic approach is used to determined the best approximate distribution of $\dfrac{Y_1}{Y_1 + Y_2}$, given that $Y_1,Y_2$ are independent, and each of $Y_1$ and $Y$ is distributed as the $\mathcal{F}$-distribution with common denominator degrees of freedom. The proposed approximate distribution is subject to graphical comparisons and distributional tests. The proposed distribution is used to derive the distribution of the elemental regression weight $\omega_E$, where $E$ is the elemental regression set., Comment: 25 pages

In this paper, we introduce reduced-bias estimators for the estimation of the tail index of a Pareto-type distribution. This is achieved through the use of a regularised weighted least squares with an exponential regression model for log-spacings of top order statistics. The asymptotic properties of the proposed estimators are investigated analytically and found to be asymptotically unbiased, consistent and normally distributed. Also, the finite sample behaviour of the estimators are studied through a simulations theory. The proposed estimators were found to yield low bias and MSE. In addition, the proposed estimators are illustrated through the estimation of the tail index of the underlying distribution of claims from the insurance industry., Comment: 24 pages

In this paper, we propose a reduced-bias estimator of the EVI for Pareto-type tails (heavy-tailed) distributions. This is derived using the weighted least squares method. It is shown that the estimator is unbiased, consistent and asymptotically normal under the second-order conditions on the underlying distribution of the data. The finite sample properties of the proposed estimator are studied through a simulation study. The results show that it is competitive to the existing estimators of the extreme value index in terms of bias and Mean Square Error. In addition, it yields estimates of $\gamma>0$ that are less sensitive to the number of top-order statistics, and hence, can be used for selecting an optimal tail fraction. The proposed estimator is further illustrated using practical datasets from pedochemical and insurance., Comment: 26 pages

Spatial telecommunication systems have evolved along the years, leading to some concerns that telecommunication companies are facing today. The main inquietude is the ability to provide quality service to customers or users in a dense regime. Therefore, questions such as : what is the best possible configurations of base stations and users that maximizes quality service? Is it possible to estimate and control the probability of bad service, which may be seen as a rare event? and many more arise. These questions often involve estimating the tail distribution of events, which falls under the scope of large deviation principles. In this article, we associate with the Boolean model, the empirical marked measure which will serve as a statistic for the intensity measure of the Marked Poisson Point Process of devices or users and the empirical connectivity measure which will serve as a statistic for coverage probability density of the spatial telecommunication area. For these empirical measures, prove large deviation principle (LDP) for well-defined empirical measures., Comment: 17 pages

The article obtains large deviation asymptotic for sub-critical communication networks modelled as signal-interference-noise-ratio(SINR) random networks. To achieve this, we define the empirical power measure and the empirical connectivity measure, as well as prove joint large deviation principles(LDPs) for the two empirical measures on two different scales. Using the joint LDPs, we prove an Asymptotic equipartition property(AEP) for wireless telecommunication Networks modelled as the subcritical SINR random networks. Further, we prove a Local Large deviation principle(LLDP) for the sub-critical SINR random network. From the LLDPs, we prove the large deviation principle, and a classical McMillan Theorem for the stochastic SINR model processes. Note that, the LDPs for the empirical measures of this stochastic SINR random network model were derived on spaces of measures equipped with the $\tau-$ topology, and the LLDPs were deduced in the space of SINR model process without any topological limitations. We motivate the study by describing a possible anomaly detection test for SINR random networks., Comment: 13 pages. arXiv admin note: text overlap with arXiv:2011.04479

In this article we obtain large deviation asymptotics for supercritical communication networks modelled as signal-interference-noise ratio networks. To do this, we define the empirical power measure and the empirical connectivity measure, and prove joint large deviation principles(LDPs) for the two empirical measures on two different scales i.e. $\lambda$ and $\lambda^2 a_{\lambda},$ where $\lambda$ is the intensity measure of the poisson point process (PPP) which defines the SINR random network.Using this joint LDPs we prove an asymptotic equipartition property for the stochastic telecommunication Networks modelled as the SINR networks. Further, we prove a Local large deviation principle(LLDP) for the SINR Network. From the LLDP we prove the a large deviation principle, and a classical MacMillian Theorem for the stochastic SNIR network processes. Note, for tupical empirical connectivity measure, $q\pi\otimes\pi,$ we can deduce from the LLDP a bound on the cardinality of the space of SINR networks to be approximately equal to $\displaystyle e^{\lambda^2 a_{\lambda}\|q\pi\otimes\pi\|H\big(q\pi\otimes\pi/\|q\pi\otimes\pi\|\big)},$ where the connectivity probability of the network, $Q^{z^\lambda} ,$ satisfies $ a_{\lambda}^{-1}Q^{z^\lambda} \to q.$ Observe, the LDP for the empirical measures of the stochastic SINR network were obtained on spaces of measures equipped with the $\tau-$ topology, and the LLDPs were obtained in the space of SINR network process without any topological restrictions., Comment: 12 pages

In this article we study the stochastic block model also known as the multi-type random networks (MRNs). For the stochastic block model or the MRNs we define the empirical group measure, empirical cooperative measure and the empirical locality measure. We derive large deviation principles for the empirical measures in the weak topology. These results will form the basis of understanding asymptotics of the evolutionary and co-evolutionary processes on the stochastic block model., Comment: 8 pages

For a $d-$regular random model, we assign to vertices $q-$state spins. From this model, we define the \emph{empirical co-operate measure}, which enumerates the number of co-operation between a given couple of spins, and \emph{ empirical spin measure}, which enumerates the number of sites having a given spin on the $d-$regular random graph model. For these empirical measures we obtain large deviation principle(LDP) in the weak topology., Comment: 5 pages

In this article we prove a Generalized Asypmtotic Equipartition Property for Networked Data Structures modelled as coloured random graphs. The main techniques in this article remains large deviation principles for suitably defined empirical measures on coloured random graphs. We apply our main result to a concrete example from the field of Biology., Comment: 9 pages

We consider preferential attachment random graphs which may be obtained as follows: It starts with a single node. If a new node appears, it is linked by an edge to one or more existing node(s) with a probability proportional to function of their degree. For a class of linear preferential attachment random graphs we find a large deviation principle (LDP) for the empirical degree measure. In the course of the prove this LDP we establish an LDP for the empirical degree and pair distribution see Theorem 2.3, of the fitness preferential attachment model of random graphs., Comment: 12 pages. arXiv admin note: substantial text overlap with arXiv:1312.1053

For fitness preferential attachment random networks, we define the empirical degree and pair measure, which counts the number of vertices of a given degree and the number of edges with given fits, and the sample path empirical degree distribution. For the empirical degree and pair distribution for the fitness preferential attachment random networks, we find a large deviation upper bound. From this result we obtain a weak law of large numbers for the empirical degree and pair distribution, and the basic information theorem or an asymptotic equipartition property for fitness preferential attachment random networks., Comment: 11 pages

In this article we find exponential good approximation of the empirical neigbourhood distribution of symbolled random graphs conditioned to a given empirical symbol distribution and empirical pair distribution. Using this approximation we shorten or simplify the proof of (Doku-Amponsah and Morters 2010, Theorem~2.5); the large deviation principle (LDP) for empirical neigbourhood distribution of symbolled random graphs. We also show that the LDP for the empirical degree measure of the classical Erd\H{o}s-R\'{e}nyi graph is a special case of (Doku-Amponsah and Moerters, 2010, Theorem~2.5). From the LDP for the empirical degree measure, we derive an LDP for the the proportion of isolated vertices in the classical Erd\H{o}s-R\'{e}nyi graph., Comment: 11 pages