scholarly journals On power-law tail distribution of strength statistics of brittle and quasibrittle structures

2018 ◽  
Vol 197 ◽  
pp. 80-91 ◽  
Author(s):  
Zhifeng Xu ◽  
Jia-Liang Le
Keyword(s):  
Power Law ◽  
Tail Distribution

scholarly journals An Agent-Based Model of a Pricing Process with Power Law, Volatility Clustering, and Jumps

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Yu Shi ◽  
Qixuan Luo ◽  
Handong Li
Keyword(s):  
Random Walk ◽  
Power Law ◽  
Asset Prices ◽  
Structural Changes ◽  
Power Law Distribution ◽  
Volatility Clustering ◽  
Agent Based ◽  
Tail Distribution ◽  
Pricing Process ◽  
Security Price

In this paper, we propose a new model of security price dynamics in order to explain the stylized facts of the pricing process such as power law distribution, volatility clustering, jumps, and structural changes. We assume that there are two types of agents in the financial market: speculators and fundamental investors. Speculators use past prices to predict future prices and only buy assets whose prices are expected to rise. Fundamental investors attach a certain value to each asset and buy when the asset is undervalued by the market. When the expectations of agents are exogenously driven, that is, entirely shaped by exogenous news, then they can be modeled as following a random walk. We assume that the information related to the two types of agents in the model will arrive randomly with a certain probability distribution and change the viewpoint of the agents according to a certain percentage. Our simulated results show that this model can simulate well the random walk of asset prices and explain the power-law tail distribution of returns, volatility clustering, jumps, and structural changes of asset prices.

scholarly journals Large Cliques in a Power-Law Random Graph

2010 ◽  
Vol 47 (4) ◽  
pp. 1124-1135 ◽  
Author(s):  
Svante Janson ◽  
Tomasz Łuczak ◽  
Ilkka Norros
Keyword(s):  
Random Graph ◽  
Power Law ◽  
High Probability ◽  
Simple Algorithm ◽  
Heavy Tail ◽  
Degree Sequences ◽  
Constant Size ◽  
Tail Distribution ◽  
Law Degree ◽  
Large Clique

In this paper we study the size of the largest clique ω(G(n, α)) in a random graph G(n, α) on n vertices which has power-law degree distribution with exponent α. We show that, for ‘flat’ degree sequences with α > 2, with high probability, the largest clique in G(n, α) is of a constant size, while, for the heavy tail distribution, when 0 < α < 2, ω(G(n, α)) grows as a power of n. Moreover, we show that a natural simple algorithm with high probability finds in G(n, α) a large clique of size (1 − o(1))ω(G(n, α)) in polynomial time.

scholarly journals Large Cliques in a Power-Law Random Graph

2010 ◽  
Vol 47 (04) ◽  
pp. 1124-1135 ◽  
Author(s):  
Svante Janson ◽  
Tomasz Łuczak ◽  
Ilkka Norros
Keyword(s):  
Random Graph ◽  
Power Law ◽  
High Probability ◽  
Simple Algorithm ◽  
Heavy Tail ◽  
Degree Sequences ◽  
Constant Size ◽  
Tail Distribution ◽  
Law Degree ◽  
Large Clique

In this paper we study the size of the largest clique ω(G(n, α)) in a random graph G(n, α) on n vertices which has power-law degree distribution with exponent α. We show that, for ‘flat’ degree sequences with α &gt; 2, with high probability, the largest clique in G(n, α) is of a constant size, while, for the heavy tail distribution, when 0 &lt; α &lt; 2, ω(G(n, α)) grows as a power of n. Moreover, we show that a natural simple algorithm with high probability finds in G(n, α) a large clique of size (1 − o(1))ω(G(n, α)) in polynomial time.

Statistical properties of the mutual transfer network among global football clubs

2018 ◽  
Vol 32 (29) ◽  
pp. 1850320 ◽  
Author(s):  
Ming-Xia Li ◽  
Qiao-Li Xiao ◽  
Yue Wang ◽  
Wei-Xing Zhou
Keyword(s):  
Power Law ◽  
Node Degree ◽  
Edge Weight ◽  
Football Players ◽  
Football Clubs ◽  
Tail Distribution ◽  
The World ◽  
Different Types ◽  
Topological Characteristics ◽  
Nonlinear Correlations

Football is the most popular sport in the world, and one of the most interesting events is the transferring of football players among various clubs. Based on 470,792 transfer records among 23,605 football clubs in 206 countries and regions, we construct a mutual transfer network and investigate its basic topological characteristics related to node degree k, edge weight w and node strength s. We find that the distributions can be well fitted by bimodal distributions for k and s or a power-law tail distribution for w. By studying the features of neighbor nodes or edges, we find that the mutual transfer network exhibits assortative mixing for most nodes or clubs but disassortative for clubs with very large degrees. We also observe nonlinear correlations among the different types of measures. Our work sheds new lights into the investigation of the characteristics of football transfer activities.

Analysis of power law assumptions for short-period comets and Kuiper bodies

1999 ◽  
Vol 173 ◽  
pp. 289-293 ◽  
Author(s):  
J.R. Donnison ◽  
L.I. Pettit
Keyword(s):  
Power Law ◽  
Pareto Distribution ◽  
Limited Data ◽  
Short Period ◽  
Probability Plots ◽  
Exponential Probability

AbstractA Pareto distribution was used to model the magnitude data for short-period comets up to 1988. It was found using exponential probability plots that the brightness did not vary with period and that the cut-off point previously adopted can be supported statistically. Examination of the diameters of Trans-Neptunian bodies showed that a power law does not adequately fit the limited data available.

Hearing in Mice by GSR Audiometry: II. Magnitude of Unconditioned GSR as a Function of Intensity and Frequency Interactions

1968 ◽  
Vol 11 (1) ◽  
pp. 169-178 ◽  
Author(s):  
Alan Gill ◽  
Charles I. Berlin
Keyword(s):  
Power Law ◽  
Response Magnitude ◽  
Single Units ◽  
Spectral Content ◽  
Hearing Sensitivity ◽  
Subjective Magnitude ◽  
Orderly Fashion ◽  
Viiith Nerve

The unconditioned GSR’s elicited by tones of 60, 70, 80, and 90 dB SPL were largest in the mouse in the ranges around 10,000 Hz. The growth of response magnitude with intensity followed a power law (10 .17 to 10 .22 , depending upon frequency) and suggested that the unconditioned GSR magnitude assessed overall subjective magnitude of tones to the mouse in an orderly fashion. It is suggested that hearing sensitivity as assessed by these means may be closely related to the spectral content of the mouse’s vocalization as well as to the number of critically sensitive single units in the mouse’s VIIIth nerve.

How Useful is the Power Law of Practice for Recognizing Practice in Concentration Tests?

2007 ◽  
Vol 23 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Carmen Hagemeister
Keyword(s):  
Logistic Regression ◽  
Reaction Time ◽  
Power Law ◽  
Error Rate ◽  
Reaction Times ◽  
Practice Effect ◽  
Practice Effects ◽  
Rate Decrease ◽  
Small Practice

Abstract. When concentration tests are completed repeatedly, reaction time and error rate decrease considerably, but the underlying ability does not improve. In order to overcome this validity problem this study aimed to test if the practice effect between tests and within tests can be useful in determining whether persons have already completed this test. The power law of practice postulates that practice effects are greater in unpracticed than in practiced persons. Two experiments were carried out in which the participants completed the same tests at the beginning and at the end of two test sessions set about 3 days apart. In both experiments, the logistic regression could indeed classify persons according to previous practice through the practice effect between the tests at the beginning and at the end of the session, and, less well but still significantly, through the practice effect within the first test of the session. Further analyses showed that the practice effects correlated more highly with the initial performance than was to be expected for mathematical reasons; typically persons with long reaction times have larger practice effects. Thus, small practice effects alone do not allow one to conclude that a person has worked on the test before.

Inverse Power Law Behavior in a Memory Scanning Experiment

2006 ◽  
Author(s):  
Gerardo Ramirez ◽  
Sonia Perez ◽  
John G. Holden
Keyword(s):  
Power Law ◽  
Inverse Power ◽  
Memory Scanning ◽  
Inverse Power Law
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