A Myth of Preferred Days of Strong Earthquakes?

2020 ◽  
Vol 91 (2A) ◽  
pp. 948-955 ◽  
Author(s):  
Vladimir G. Kossobokov ◽  
Giuliano F. Panza
Keyword(s):  
Seasonal Pattern ◽  
Statistical Testing ◽  
Test Statistics ◽  
The Moon ◽  
Origin Time ◽  
Empirical Distributions ◽  
Gravitational Loading ◽  
Kolmogorov Smirnov ◽  
Cyclic Variations ◽  
Kuiper Test

Abstract Existing evidence on the origin times of magnitude M≥7.5 earthquakes worldwide, based on authoritative earthquake catalogs, does not permit rejection of the null hypotheses of random coincidental occurrence at any time during the Earth or the Moon cycles. Specifically, the nonparametric Kuiper test statistics for cyclic variations applied to seismic evidence resulting from the empirical distributions of the M≥7.5 earthquake origin time Julian day (JD) and the Moon phase (MP) do not allow the rejection of the null hypotheses of uniform distributions within the corresponding cycles. On the other hand, the same Kuiper test permits the rejection of the null hypotheses of the same chance of occurrence on any JD or MP for strong magnitude M≥6.0 earthquakes, at least for the past four decades of presumably the best-earthquake determinations and, in particular, for earthquakes in the Northern Hemisphere (with an evident seasonal pattern). The nonparametric two-sample Kolmogorov–Smirnov test statistics suggest no preferred distances to the Moon at the occurrence of large earthquakes. All of this contributes, to the results of solid statistical testing of hypotheses, for a better understanding of the complex seismic response of the Earth’s lithosphere to periodic gravitational loading.

Earthquake preferred days in the Lake Baikal and Yunnan-Sichuan Regions

2020 ◽  
Author(s):  
Vladimir Kossobokov ◽  
Polina Schepalina ◽  
Anastasia Nekrasova
Keyword(s):  
Lake Baikal ◽  
Plate Tectonics ◽  
Test Statistics ◽  
Origin Time ◽  
Empirical Distributions ◽  
The Earth ◽  
Test Application ◽  
Global Cooling ◽  
Cyclic Variations ◽  
Kuiper Test

<p>Understanding the cyclic and other forces governing geodynamics may provide fundamental clues for unraveling characteristics of earthquakes occurrence, which remains spectacular evidence induced by plate tectonics fueled by tidal drag and associated global cooling of the Earth (Riguzzi et al., 2010; Doglioni and Panza, 2015).</p><p>To check the hypotheses of earthquake-preferred days the nonparametric Kuiper test statistics for cyclic variations applied to the seismic evidence resulting from the empirical distributions of the earthquake origin time versus solar (Julian Day, JD) or lunar (Moon Phase, MP) cycles. We present the results of the Kuiper test application to seismicity of the Lake Baikal and Yúnnán-Sichuan Regions aimed at verification on a solid statistical base the hypotheses of uniform distribution of earthquake origin time JD’s and MP’s in respect to the earthquake magnitude cut-off.</p>

scholarly journals Penerapan Model Pembelajaran Problem Based Learning (PBL) terhadap Hasil Belajar Siswa SMAN 7 Palu Kelas XI pada Materi Larutan Penyangga

2018 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Amsar T. Beddu ◽  
Sri Mulyani Sabang ◽  
Purnama Ningsih
Keyword(s):  
Problem Based Learning ◽  
Positive Influence ◽  
Control Group ◽  
Homogeneity Test ◽  
Distributed Data ◽  
Test Statistics ◽  
Control Group Design ◽  
Average Value ◽  
Kolmogorov Smirnov ◽  
Normally Distributed

This research aims to investigate the result of a study in applying the model of problem-based learning (PBL) the students of SMAN 7 Palu on the topic buffer. This method use quasy experiment with pretest-posttest control group design. The population is a class XI students of SMAN 7 Palu, where the sample is a 23 students in class XI IPA 3 as the experimental group and 23 students in class XI IPA 4 as the control group, which is determined by purposive sampling. The result of student learning to the average value of the experimental class is bigger than for class control experiment was 82.61 and for the control, class is 8.65. Test normality using the Kolmogorov-Smirnov test values obtained for the experimental class Sig 0.20 > 0.05 and class control 0.20 > 0.05, it means that both at data are normally distributed. Homogeneity test using the test statistics Lavene which gained value of Fhitung 0.37 > 0.54 Ftabel for experimental classes.200 Sig > 0.05 and for grade control 0.20 > 0.05, it means that both of normally distributed data, there is similarities variant between a group or the means homogeneous. Testing hypothesis use t-test two parties where the significance value of 0.06, it means at the value of Sig > 0.05, that Ho rejected and Ha accepted. Based on the statistical test, PBL learning model on material buffer has a positive influence on results for students at SMAN 7 Palu

scholarly journals Probabilistic Modeling of Monthly Temperature Historical Series in Mossoró, Northeastern Brazil

2018 ◽  
Vol 10 (12) ◽  
pp. 534
Author(s):  
Janilson Pinheiro de Assis ◽  
Roberto Pequeno de Sousa ◽  
Ben Deivide de Oliveira Batista ◽  
Paulo César Ferreira Linhares ◽  
Eudes de Almeida Cardoso ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Northeastern Brazil ◽  
Chi Square ◽  
Empirical Distributions ◽  
Monthly Temperature ◽  
Small Series ◽  
Pearson Type ◽  
Historical Series ◽  
Kolmogorov Smirnov ◽  
Von Mises

We fitted the following seven distribution probabilities to the data of monthly average temperature in Mossoró, northeastern Brazil: Normal, Log-Normal, Beta, Gamma, Log-Pearson (Type III), Gumbel, and Weibull. To assess the goodness of fit the empirical distributions to the theoretical distribution, we applied the tests of Kolmogorov-Smirnov, Chi-square, Cramer-von Mises, Anderson-Darling, Kuiper, and Logarithm of Maximum Likelihood, at 10% of probability. The temperature series were obtained from 1970 to 2007. The Normal distribution provided the best fit to the historical series of average monthly temperature. Although the Kolmogorov-Smirnov test showed a very high level of approval, which generated some uncertainty regarding the test criteria, it is the more recommended to studies with approximately symmetric data and small series.

PaleoJump database for research on rapid climate transitions

2021 ◽  
Author(s):  
Witold Bagniewski ◽  
Denis-Didier Rousseau ◽  
Michael Ghil
Keyword(s):  
Time Intervals ◽  
Origin Time ◽  
Essential Information ◽  
The Earth ◽  
Dating Methods ◽  
Long Time ◽  
Kolmogorov Smirnov ◽  
Paleoclimate Records ◽  
Smirnov Test ◽  
Climate Transitions

<p><span>Tipping poi</span><span>nts (TPs) in the </span><span>Earth system have been studied with growing interest and concern in recent years due to the potential risk of anthropogenic forcing causing abrupt, and possibly irreversible, climate transitions. Paleoclimate records are essential for identifying TPs in the Earth’s past and to properly understand the climate system’s underlying bifurcation mechanisms. </span><span>Due to their varying quality, resolution, and dating methods, it is often necessary to select the records that give the best representation of past climate. Furthermore, as paleoclimate records vary in their origin, time spans, and periodicities, an objective, automated methodology is crucial for identifying and comparing </span><span>TP</span><span>s. </span></p><p><span>To reach this goal, here we present the PaleoJump database of carefully selected, high-resolution records originating in ice, marine sediments, speleothems, loess, and lake sediments. These records, which include tipping elements, cover long time intervals and represent a global distribution from all continents and ocean basins. For every record, a transition detection methodology based on an augmented Kolmogorov-Smirnov test is applied to identify abrupt transitions. The PaleoJump database </span><span>highlights</span><span> these automatically detected transitions for every record together with other essential information, including location, temporal scale and resolution, as well as temporal plots; it therefore represents a valuable resource for researchers investigating TPs in past climates. This study is supported by</span> <span>the H2020-funded</span> <span>TiPES project.</span></p>

scholarly journals A Variance Shift Model for Detection of Outliers in the Linear Measurement Error Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Babak Babadi ◽  
Abdolrahman Rasekh ◽  
Ali Akbar Rasekhi ◽  
Karim Zare ◽  
Mohammad Reza Zadkarami
Keyword(s):  
Measurement Error ◽  
Score Test ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Linear Measurement ◽  
Error Model ◽  
Test Statistics ◽  
Measurement Error Model ◽  
Empirical Distributions ◽  
Shift Model

We present a variance shift model for a linear measurement error model using the corrected likelihood of Nakamura (1990). This model assumes that a single outlier arises from an observation with inflated variance. The corrected likelihood ratio and the score test statistics are proposed to determine whether theith observation has an inflated variance. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to show the performance of proposed tests. Finally, a real data example is given for illustration.

scholarly journals Goodness-of-fit tests for the beta Gompertz distribution

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 69-81
Author(s):  
Hanaa Abu-Zinadah ◽  
Asmaa Binkhamis
Keyword(s):  
Goodness Of Fit ◽  
Real Data ◽  
Likelihood Method ◽  
Test Statistics ◽  
Gompertz Distribution ◽  
Goodness Of Fit Tests ◽  
Lilliefors Test ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Von Mises

This article studied the goodness-of-fit tests for the beta Gompertz distribution with four parameters based on a complete sample. The parameters were estimated by the maximum likelihood method. Critical values were found by Monte Carlo simulation for the modified Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises, and Lilliefors test statistics. The power of these test statistics founded the optimal alternative distribution. Real data applications were used as examples for the goodness of fit tests.

Abstract 17352: Seasonal Trends of Acute Idiopathic Pericardial Effusion Requiring Pericardial Drainage

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Muhammad M Furqan ◽  
Abdullah Yesilyaprak ◽  
Beni R Verma ◽  
Hassan Mehmood Lak ◽  
Dakshin Gangadharamurthy ◽  
...  
Keyword(s):  
Pericardial Effusion ◽  
Lupus Erythematosus ◽  
Viral Infections ◽  
Seasonal Pattern ◽  
Test Statistics ◽  
Chi Square ◽  
Pericardial Window ◽  
Seasonal Trends ◽  
Pericardial Drainage ◽  
Chi Square Test

Background: The seasonal trends of idiopathic pericardial effusion (PEff) are not known. Small PEff is usually asymptomatic but moderate to large PEff may lead to cardiac tamponade necessitating pericardial drainage procedures. Seasonal variations of PEff can help identify the association with viral infections that follow a seasonal pattern. Therefore, we sought to characterize the seasonal trends of moderate to large PEff. Methods: We retrospectively identified pericardial effusion patients from January 2015 to December 2019. Moderate to large PEff was defined as PEff requiring either pericardiocentesis or pericardial window. Patients with minimal to small PEff, autoimmune diseases (systemic lupus erythematosus, rheumatoid arthritis, systemic sclerosis), cardiac surgeries/invasive procedures, hemopericardium, chronic pericarditis, malignancy, and metastasis were excluded. Data was acquired from electronic medical records and frequencies, means, percentages, and chi-square test statistics were calculated. Results: Of the 472 patients with PEff, 63% (n=296) were males and 37% (n=176) females. The median age was 61 years. Pericardiocentesis was performed in 65% of patients and 35% had pericardial window. All seasons had similar incidence of PEff (winter 27%, spring 25%, summer 24%, fall 23%, [X 2 =1.81, p=0.612]). The incidence also remained same across all the quarters of the year (Q1 25%, Q2 25%, Q3 26%, Q4 24%, [X2=0.119, p=0.990). The incidence of pericardiocentesis and pericardial window in winter (27% vs 27.5), Spring (22% vs 31%), summer (26% vs 20%), fall (25% vs 20%) showed no difference (X2=6.40, p=0.094). Conclusion: The incidence of moderate to large acute idiopathic pericardial effusion is consistent across all seasons and quarters. Similarly, no significant seasonality was associated with pericardiocentesis and pericardial window procedures.

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