Multiply broken power-law densities as survival functions: An alternative to Pareto and lognormal fits

Tomaschitz, Roman

doi:10.1016/j.physa.2019.123188

Cited by 16 publications

(4 citation statements)

References 40 publications

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“…Moreover, is there a distribution that could fit the whole range of the data, and not focus on the upper or lower tail? Recently, papers have also begun exploring alternatives to the power law distribution in many empirical settings and also considering the fit below the upper tail (e.g., the size of cities [43,12,61,53], the size of business firms [62]).…”

Section: Introductionmentioning

confidence: 99%

The distribution of strike size: Empirical evidence from Europe and North America in the 19th and 20th centuries

Campolieti

Ramos

2021

Physica A: Statistical Mechanics and its Applications

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Section: Introductionmentioning

confidence: 99%

The distribution of strike size: Empirical evidence from Europe and North America in the 19th and 20th centuries

Campolieti

Ramos

2021

Physica A: Statistical Mechanics and its Applications

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“…The probability distribution of the dwell times is shown in Fig. 4, which shows that the data for short dwell times obey a broken power-law distribution [44], with two power-law segments broken at the point of 24 min. For long dwell times (more than 13 h), the distribution fluctuates greatly with no obvious pattern.…”

Section: Defining Time Thresholdsmentioning

confidence: 97%

Identifying intercity freight trip ends of heavy trucks from GPS data

Yang¹,

Jia²,

Yan³

et al. 2021

Preprint

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The intercity freight trips of heavy trucks are important data for transportation system planning and urban agglomeration management. In recent decades, the extraction of freight trips from GPS data has gradually become the main alternative to traditional surveys. Identifying the trip ends (origin and destination, OD) is the first task in trip extraction. In previous trip end identification methods, some key parameters, such as speed and time thresholds, have mostly been defined on the basis of empirical knowledge, which inevitably lacks universality. Here, we propose a data-driven trip end identification method. First, we define a speed threshold by analyzing the speed distribution of heavy trucks and identify all truck stops from raw GPS data.Second, we define minimum and maximum time thresholds by analyzing the distribution of the dwell times of heavy trucks at stop location and classify truck stops into three types based on these time thresholds. Third, we use highway network GIS data and freight-related points-of-interest (POIs) data to identify valid trip ends from among the three types of truck stops. In this step, we detect POI boundaries to determine whether a heavy truck is stopping at a freight-related location. We further analyze the spatiotemporal characteristics of intercity freight trips of heavy trucks and discuss their potential applications in practice.

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“…However, one issue that has been highlighted in the research literature is that it can be difficult to distinguish a power law with the upper tail of other heavy tailed distributions [14,23,4,7,3,5,6,11,25,33]. This means that there could be other distributions that are a plausible fit to the upper tail of the data.…”

Section: The Distributionsmentioning

confidence: 99%

Comment on “A statistical evidence of power law distribution in the upper tail of world billionaires’ data 2010–20” [Physica A 581 (2021) 126198]

Ramos

2022

Physica A: Statistical Mechanics and its Applications

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Multiply broken power-law densities as survival functions: An alternative to Pareto and lognormal fits

Cited by 16 publications

References 40 publications

The distribution of strike size: Empirical evidence from Europe and North America in the 19th and 20th centuries

The distribution of strike size: Empirical evidence from Europe and North America in the 19th and 20th centuries

Identifying intercity freight trip ends of heavy trucks from GPS data

Comment on “A statistical evidence of power law distribution in the upper tail of world billionaires’ data 2010–20” [Physica A 581 (2021) 126198]

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