POWER LAWS, WEAK lp ESTIMATE AND WAVELET DE-NOISING

Fractals ◽  
2000 ◽  
Vol 08 (01) ◽  
pp. 73-83
Author(s):  
TOMOHIRO MATSUOKA ◽  
TOSHIHIDE UENO ◽  
TAKASHI ADACHI ◽  
MASAMI OKADA

Data with power law distributions are studied by a scaling argument. Then related weak lp sequences are characterized. As an application we can show in a transparent way that the wavelet de-noising theory holds under a mild assumption which is given by means of weak lp (quasi-)norms.

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2725-2729 ◽  
Author(s):  
NING DING ◽  
YOUGUI WANG ◽  
JUN XU ◽  
NING XI

We introduce preferential behavior into the study on statistical mechanics of money circulation. The computer simulation results show that the preferential behavior can lead to power laws on distributions over both holding time and amount of money held by agents. However, some constraints are needed in generation mechanism to ensure the robustness of power-law distributions.


Author(s):  
M. E. J. Newman ◽  
R. G. Palmer

In chapters 2 to 4 we discussed several models of extinction which make use of ideas drawn from the study of critical phenomena. The primary impetus for this approach was the observation of apparent power-law distributions in a variety of statistics drawn from the fossil record, as discussed in section 1.2; in other branches of science such power laws are often indicators of critical processes. However, there are also a number of other mechanisms by which power laws can arise, including random multiplicative processes (Montroll and Shlesinger 1982; Sornette and Cont 1997), extremal random processes (Sibani and Littlewood 1993), and random barrier-crossing dynamics (Sneppen 1995). Thus the existence of power-law distributions in the fossil data is not on its own sufficient to demonstrate the presence of critical phenomena in extinction processes. Critical models also assume that extinction is caused primarily by biotic effects such as competition and predation, an assumption which is in disagreement with the fossil record. As discussed in section 1.2.2.1, all the plausible causes for specific prehistoric extinctions are abiotic in nature. Therefore an obvious question to ask is whether it is possible to construct models in which extinction is caused by abiotic environmental factors, rather than by critical fluctuations arising out of biotic interactions, but which still give power-law distributions of the relevant quantities. Such models have been suggested by Newman (1996, 1997) and by Manrubia and Paczuski (1998). Interestingly, both of these models are the result of attempts at simplifying models based on critical phenomena. Newman's model is a simplification of the model of Newman and Roberts (see section 3.6), which included both biotic and abiotic effects; the simplification arises from the realization that the biotic part can be omitted without losing the power-law distributions. Manrubia and Paczuski's model was a simplification of the connection model of Solé and Manrubia (see section 4.1), but in fact all direct species-species interactions were dropped, leaving a model which one can regard as driven only by abiotic effects. We discuss these models in turn. The model proposed by Newman (1996, 1997) has a fixed number N of species which in the simplest case are noninteracting.


2003 ◽  
Vol 06 (02) ◽  
pp. 215-222 ◽  
Author(s):  
G. J. RODGERS ◽  
Y. J. YAP ◽  
T. P. YOUNG

Motivated by recent empirical studies of the length distribution of hospital waiting lists, we introduce and solve a set of models that imitate the formation of waiting lists. Patients arriving in the system must choose a waiting list to join, based on its length. At the same time patients leave the lists as they get served. The model illustrates how the power-law distributions found in the empirical studies might arise, but indicates that the mechanism causing the power-laws is unlikely to be the preferential behavior of patients or their physicians.


2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Nicole Eikmeier ◽  
David F Gleich

Abstract Preferential attachment (PA) models are a common class of graph models which have been used to explain why power-law distributions appear in the degree sequences of real network data. Among other properties of real-world networks, they commonly have non-trivial clustering coefficients due to an abundance of triangles as well as power laws in the eigenvalue spectra. Although there are triangle PA models and eigenvalue power laws in specific PA constructions, there are no results that existing constructions have both. In this article, we present a specific Triangle Generalized Preferential Attachment Model that, by construction, has non-trivial clustering. We further prove that this model has a power law in both the degree distribution and eigenvalue spectra.


2012 ◽  
Vol 12 (3) ◽  
pp. 721-731
Author(s):  
Reinhard Mahnke ◽  
Jevgenijs Kaupužs ◽  
Mārtiņš Brics

AbstractPower-law distributions and other skew distributions, observed in various models and real systems, are considered. A model, describing evolving systems with increasing number of elements, is considered to study the distribution over element sizes. Stationary power-law distributions are found. Certain non-stationary skew distributions are obtained and analyzed, based on exact solutions and numerical simulations.


2020 ◽  
Vol 14 (3-4) ◽  
pp. 186
Author(s):  
Andrew Gustar

This paper investigates the processes leading to musical fame or obscurity, whether for composers, performers, or works themselves. It starts from the observation that the patterns of success, across many historical music datasets, follow a similar mathematical relationship known as a power law, often with an exponent approximately equal to two. It presents several simple models which can produce power law distributions. An examination of these models' transience characteristics suggests parallels with some historical music examples, giving clues to the ways that success and obscurity might emerge in practice and the extent to which success might be influenced by inherent musical quality. These models can be seen as manifestations of a more fundamental process resulting from the law of maximum entropy, subject to a constraint on the average value of the logarithm of the success measure. This implies that musical success is a multiplicative quality, and suggests that musical markets operate to strike a balance between familiarity (socio-cultural importance) and novelty (individual importance). The common power law exponent of two is seen to emerge as a consequence of the tendency for musical activity to be spread evenly across the log-success bands.


Fractals ◽  
2015 ◽  
Vol 23 (02) ◽  
pp. 1550009 ◽  
Author(s):  
YANGUANG CHEN

The difference between the inverse power function and the negative exponential function is significant. The former suggests a complex distribution, while the latter indicates a simple distribution. However, the association of the power-law distribution with the exponential distribution has been seldom researched. This paper is devoted to exploring the relationships between exponential laws and power laws from the angle of view of urban geography. Using mathematical derivation and numerical experiments, I reveal that a power-law distribution can be created through a semi-moving average process of an exponential distribution. For the distributions defined in a one-dimension space (e.g. Zipf's law), the power exponent is 1; while for those defined in a two-dimension space (e.g. Clark's law), the power exponent is 2. The findings of this study are as follows. First, the exponential distributions suggest a hidden scaling, but the scaling exponents suggest a Euclidean dimension. Second, special power-law distributions can be derived from exponential distributions, but they differ from the typical power-law distributions. Third, it is the real power-law distributions that can be related with fractal dimension. This study discloses an inherent link between simplicity and complexity. In practice, maybe the result presented in this paper can be employed to distinguish the real power laws from spurious power laws (e.g. the fake Zipf distribution).


2019 ◽  
Author(s):  
Kristina Grigaityte ◽  
Gurinder Atwal

AbstractObserved data from many research disciplines, ranging from cellular biology to economics, often follow a particular long-tailed distribution known as a power law. Despite the ubiquity of natural power laws, inferring the exact form of the distribution from sampled data remains challenging. The possible presence of multiple generative processes giving rise to an unknown weighted mixture of distinct power law distributions in a single dataset presents additional challenges. We present a probabilistic solution to these issues by developing a Bayesian inference approach, with Markov chain Monte Carlo sampling, to accurately estimate power law exponents, the number of mixtures, and their weights, for both discrete and continuous data. We determine an objective prior distribution that is invariant to reparameterization of parameters, and demonstrate its effectiveness to accurately infer exponents, even in the low sample limit. Finally, we provide a comprehensive and documented software package, written in Python, of our Bayesian inference methodology, freely available at https://github.com/AtwalLab/BayesPowerlaw.


2021 ◽  
Vol 8 (7) ◽  
pp. 210850
Author(s):  
P. L. Ramos ◽  
L. F. Costa ◽  
F. Louzada ◽  
F. A. Rodrigues

The Roman Empire shaped western civilization, and many Roman principles are embodied in modern institutions. Although its political institutions proved both resilient and adaptable, allowing it to incorporate diverse populations, the Empire suffered from many conflicts. Indeed, most emperors died violently, from assassination, suicide or in battle. These conflicts produced patterns in the length of time that can be identified by statistical analysis. In this paper, we study the underlying patterns associated with the reign of the Roman emperors by using statistical tools of survival data analysis. We consider all the 175 Roman emperors and propose a new power-law model with change points to predict the time-to-violent-death of the Roman emperors. This model encompasses data in the presence of censoring and long-term survivors, providing more accurate predictions than previous models. Our results show that power-law distributions can also occur in survival data, as verified in other data types from natural and artificial systems, reinforcing the ubiquity of power-law distributions. The generality of our approach paves the way to further related investigations not only in other ancient civilizations but also in applications in engineering and medicine.


Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl

Scaling appears practically everywhere in science; it basically quantifies how the properties or shapes of an object change with the scale of the object. Scaling laws are always associated with power laws. The scaling object can be a function, a structure, a physical law, or a distribution function that describes the statistics of a system or a temporal process. We focus on scaling laws that appear in the statistical description of stochastic complex systems, where scaling appears in the distribution functions of observable quantities of dynamical systems or processes. The distribution functions exhibit power laws, approximate power laws, or fat-tailed distributions. Understanding their origin and how power law exponents can be related to the particular nature of a system, is one of the aims of the book.We comment on fitting power laws.


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