Statistical Tests for Scaling in the Inter-Event Times of Earthquakes in California

We explore in depth the validity of a recently proposed scaling law for earthquake interevent time distributions in the case of the Southern California, using the waveform cross-correlation catalog of Shearer et al. Two statistical tests are used: on the one hand, the standard two-sample Kolmogorov-Smirnov test is in agreement with the scaling of the distributions. On the other hand, the one-sample Kolmogorov-Smirnov statistic complemented with Monte Carlo simulation of the inter-event times, as done by Clauset et al., supports the validity of the gamma distribution as a simple model of the scaling function appearing on the scaling law, for rescaled inter-event times above 0.01, except for the largest data set (magnitude greater than 2). A discussion of these results is provided.Comment: proceedings of Erice conference, 200

Comment on "Do Earthquakes Exhibit Self-Organized Criticality?"

It is shown that earthquakes do not know how large they will become, at least from the information collected at seismic catalogs. In other words, the magnitude is independent on previous magnitudes as well as on the waiting time between previous earthquakes. In contrast, the time to the next event does depend on the magnitude. Also it is argued that SOC systems do not necessarily shown a Poisson-type behavior in time, and SOC does not exclude the possibility of some degree of prediction.Comment: Tentative comment to Yang, Du, Ma, PRL 92, 228501 (2004

Large-scale analysis of Zipf's law in English texts

Despite being a paradigm of quantitative linguistics, Zipf's law for words suffers from three main problems: its formulation is ambiguous, its validity has not been tested rigorously from a statistical point of view, and it has not been confronted to a representatively large number of texts. So, we can summarize the current support of Zipf's law in texts as anecdotic. We try to solve these issues by studying three different versions of Zipf's law and fitting them to all available English texts in the Project Gutenberg database (consisting of more than 30000 texts). To do so we use state-of-the art tools in fitting and goodness-of-fit tests, carefully tailored to the peculiarities of text statistics. Remarkably, one of the three versions of Zipf's law, consisting of a pure power-law form in the complementary cumulative distribution function of word frequencies, is able to fit more than 40% of the texts in the database (at the 0.05 significance level), for the whole domain of frequencies (from 1 to the maximum value) and with only one free parameter (the exponent)

The Wall between Latinas and Latinos? Gender and Immigration Enforcement Attitudes among U.S. Latina/o Voters

Donald Trump’s surprising level of support among U.S. Latina/o voters in 2016 and his improved performance in the 2020 election posed a puzzle for Latina/o politics scholars given his stridently anti-immigrant agenda. Although scholars have acknowledged the political gender gap between Latinas and Latino men, few studies have outlined the theoretical basis or explored the empirical existence of gender differences in Latina/o immigration enforcement attitudes. Building on the Latina politics literature documenting Latinas’ greater engagement in solidarity work with immigrants and their greater desire for cultural transmission and the maintenance of pan-ethnic identity, I test two hypotheses. The first (the Latina/o gender hypothesis) postulates that Latinas will exhibit more liberal attitudes on matters of immigration enforcement relative to Latino men. The second (the immigrant identity hypothesis) postulates that Latinas are more likely to rely on their sense of commonality with immigrants in the formation of their immigration enforcement attitudes. Bivariate and multivariate analyses of the 2020 Collaborative Multiracial Postelection Survey support both hypotheses, which suggests not only that immigration attitudes among Latinas and Latino men are meaningfully distinct, but also that there are important structural differences underlying Latina/o beliefs in this policy area

Increasing power-law range in avalanche amplitude and energy distributions

Power-law type probability density functions spanning several orders of magnitude are found for different avalanche properties. We propose a methodology to overcome empirical constrains that limit the power-law range for the distributions of different avalanche observables like amplitude, energy, duration or size. By considering catalogs of events that cover different observation windows, maximum likelihood estimation of a global power-law exponent is computed. This methodology is applied to amplitude and energy distributions of acoustic emission avalanches in failure-under- compression experiments of a nanoporous silica glass, finding in some cases global exponents in an unprecedented broad range: 4.5 decades for amplitudes and 9.5 decades for energies. In the later case, however, strict statistical analysis suggests experimental limitations might alter the power-law behavior.Comment: 23 pages, 7 figure

Ranking and significance of variable-length similarity-based time series motifs

The detection of very similar patterns in a time series, commonly called motifs, has received continuous and increasing attention from diverse scientific communities. In particular, recent approaches for discovering similar motifs of different lengths have been proposed. In this work, we show that such variable-length similarity-based motifs cannot be directly compared, and hence ranked, by their normalized dissimilarities. Specifically, we find that length-normalized motif dissimilarities still have intrinsic dependencies on the motif length, and that lowest dissimilarities are particularly affected by this dependency. Moreover, we find that such dependencies are generally non-linear and change with the considered data set and dissimilarity measure. Based on these findings, we propose a solution to rank those motifs and measure their significance. This solution relies on a compact but accurate model of the dissimilarity space, using a beta distribution with three parameters that depend on the motif length in a non-linear way. We believe the incomparability of variable-length dissimilarities could go beyond the field of time series, and that similar modeling strategies as the one used here could be of help in a more broad context.Comment: 20 pages, 10 figure