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(PDF) Completion of the standard power-law model by including upper and lower probability bounds

2003

Power-law distributions with various exponents are studied. We first introduce a simple and generic model that reproduces Zipf's law. We can regard this model both as the time evolution of the population of cities and that of the asset distribution. We show that our model is very robust against various variations. Next, we explain theoretically why our model reproduces Zipf's

Open Physics, 2023

Models of various physical, biological, and artificial phenomena follow a power law over multiple magnitudes. This article presents a modified Pareto model with an upside-down shape and an adjustable right tail. The moments, quantiles, failure rate, mean residual life, and quantile residual life functions are examined. In addition, some stochastic ordering characteristics of the proposed model are investigated. The estimation of the parameters using the maximum likelihood estimator, the mean square error, and the Anderson-Darling estimator is explored, and a simulation study is conducted to analyze their behavior. Finally, we compare the proposed model with alternative methods for describing a dataset on the strength of carbon fiber and a dataset on customer waiting times in a bank.

Physical Review E, 2019

Power-law type distributions are extensively found when studying the behaviour of many complex systems. However, due to limitations in data acquisition, empirical datasets often only cover a narrow range of observation, making it difficult to establish power-law behaviour unambiguously. In this work we present a statistical procedure to merge different datasets with the aim of obtaining a broader fitting range for the statistics of different experiments or observations of the same system or the same universality class. This procedure is applied to the Gutenberg-Richter law for earthquakes and for synthetic earthquakes (acoustic emission events) generated in the laboratory: labquakes. Different earthquake catalogs have been merged finding a Gutenberg-Ricther law holding for more than eight orders of magnitude in seismic moment. The value of the exponent of the energy distribution of labquakes depends on the material used in the compression experiments. By means of the procedure exposed in this manuscript, it has been found that the Gutenberg-Richter law for earthquakes and charcoal labquakes can be characterized by the same power-law exponent.

Physical Review E, 2012

Maximum-likelihood exponent maps have been studied as a technique to increase the understanding and improve the fit of power-law exponents to experimental and numerical simulation data, especially when they exhibit both upper and lower cutoffs. The use of the technique is tested by analysing seismological data, acoustic emission data and avalanches in numerical simulations of the 3D-Random Field Ising model. In the different examples we discuss the nature of the deviations observed in the exponent maps and some relevant conclusions are drawn for the physics behind each phenomenon.

Annals of the Institute of Statistical Mathematics, 1992

The power law process has been used to model reliability growth, software reliability and the failure times of repairable systems. This article reviews and further develops Bayesian inference for such a process. The Bayesian approach provides a unified methodology for dealing with both time and failure truncated data, As well as looking at the posterior densities of the parameters of the power law process, inference for the expected number of failures and the probability of no failures in some given time interval is discussed. Aspects of the prediction problem are examined. The results are illustrated with two data examples.

The power-law formalism was initially derived as a Taylor series approximation in logarithmic space for kinetic rate-laws. The resulting models, either as generalized mass action (GMA) or as S-systems models, allow to characterize the target system and to simulate its dynamical behavior in response to external perturbations and parameter changes. This approach has been succesfully used as a modeling tool in many applications from cell metabolism to population dynamics. Without leaving the general formalism, we recently proposed to derive the power-law representation in an alternative way that uses least-squares (LS) minimization instead of the traditional derivation based on Taylor series [B. Hernandez-Bermejo, V. Fairen, A. Sorribas, Math. Biosci. 161 (1999) 83±94]. It was shown that the resulting LS power-law mimics the target rate-law in a wider range of concentration values than the classical power-law, and that the prediction of the steady-state using the LS power-law is closer to the actual steady-state of the target system. However, many implications of this alternative approach remained to be established. We explore some of them in the present work. Firstly, we extend the de®nition of the LS power-law within a given operating interval in such a way that no preferred operating point is selected. Besides providing an alternative to the classical Taylor powerlaw, that can be considered a particular case when the operating interval is reduced to a single point, the LS power-law so de®ned is consistent with the results that can be obtained by ®tting experimental data points. Secondly, we show that the LS approach leads to a system description, either as an S-system or a GMA model, in which the systemic properties (such as the steady-state prediction or the log-gains) appear averaged over the corresponding interval when compared with the properties that can be computed from Taylorderived models in di€erent operating points within the considered operating range. Finally, we also show that the LS description leads to a global, accurate description of the system when it is submitted to external forcing.

The power-law formalism has been succesfully used as a modeling tool in many applications. The resulting models, either as Generalized Mass Action or as S-systems models, allow one to characterize the target system and to simulate its dynamical behavior in response to external perturbations and parameter changes. The power-law formalism was ®rst derived as a Taylor series approximation in logarithmic space for kinetic rate-laws. The especial characteristics of this approximation produce an extremely useful systemic representation that allows a complete system characterization. Furthermore, their parameters have a precise interpretation as local sensitivities of each of the individual processes and as rate-constants. This facilitates a qualitative discussion and a quantitative estimation of their possible values in relation to the kinetic properties. Following this interpretation, parameter estimation is also possible by relating the systemic behavior to the underlying processes. Without leaving the general formalism, in this paper we suggest deriving the power-law representation in an alternative way that uses least-squares minimization. The resulting power-law mimics the target rate-law in a wider range of concentration values than the classical power-law. Although the implications of this alternative approach remain to be established, our results show that the predicted steady-state using the least-squares power-law is closest to the actual steadystate of the target system.

Monthly Notices of the Royal Astronomical Society, 2009

Many objects studied in astronomy follow a power law distribution function, for example the masses of stars or star clusters. A still used method by which such data is analysed is to generate a histogram and fit a straight line to it. The parameters obtained in this way can be severely biased, and the properties of the underlying distribution function, such as its shape or a possible upper limit, are difficult to extract. In this work we review techniques available in the literature and present newly developed (effectively) bias-free estimators for the exponent and the upper limit. Furthermore we discuss various graphical representations of the data and powerful goodness-of-fit tests to assess the validity of a power law for describing the distribution of data. As an example, we apply the presented methods to the data set of massive stars in R136 and the young star clusters in the Large Magellanic Cloud. For R136 we confirm the result of Koen (2006) of a truncated power law with a bias-free estimate for the exponent of 2.20 ± 0.78 / 2.87 ± 0.98 (where the Salpeter-Massey value is 2.35) and for the upper limit of 143 ± 9M / 163 ± 9M , depending on the stellar models used. The star clusters in the Large Magellanic Cloud (with ages up to 10 7.5 yr) follow a truncated power law distribution with exponent 1.62 ± 0.06 and upper limit 68 ± 12 × 10 3 M . Using the graphical data representation, a significant change in the form of the mass function below 10 2.5 M can be detected, which is likely caused by incompleteness in the data.

Fractals, 2001

Power law cumulative number-size distributions are widely used to describe the scaling properties of data sets and to establish scale invariance. We derive the relationships between the scaling exponents of non-cumulative and cumulative number-size distributions for linearly binned and logarithmically binned data. Cumulative number-size distributions for data sets of many natural phenomena exhibit a "fall-off" from a power law at the largest object sizes. Previous work has often either ignored the fall-off region or described this region with a different function. We demonstrate that when a data set is abruptly truncated at large object size, fall-off from a power law is expected for the cumulative distribution. Functions to describe this fall-off are derived for both linearly and logarithmically binned data. These functions lead to a generalized function, the upper-truncated power law, that is independent of binning method. Fitting the upper-truncated power law to a cumul...

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