scholarly journals Asymptotic theory for the inference of the latent trawl model for extreme values

2021 ◽  
Author(s):  
Valentin Courgeau ◽  
Almut E. D. Veraart
Keyword(s):  
Asymptotic Theory ◽  
Extreme Values

scholarly journals Application of the asymptotic theory of extreme values to risk simulation of breaking-out of large forest fires

2021 ◽  
Vol 34 (02) ◽  
pp. 1021-1031
Author(s):  
Vladimir Yu. Kosygin ◽  
Viktor D. Katin ◽  
Midkhat H. Akhtyamov ◽  
Mihail V. Ilyavin
Keyword(s):  
General Population ◽  
Probability Distribution ◽  
Forest Fires ◽  
Asymptotic Theory ◽  
Extreme Values ◽  
Original Material ◽  
Distribution Analysis ◽  
Sample Analysis ◽  
The South ◽  
Risk Simulation

Issues of applying the asymptotic theory of extreme values to the risks analysis of breaking-out of the largest-area yearly forest fires, were considered. As original material, the paper authors used data on areas of the forest fires occurring in the south of Russia’s Khabarovsk Territory from 1968 to 2017. For each year, the largest-area forest fire was selected from the considered period of time. In all, 50 fires were selected for this period of time (according to quantity of the years in the period). This sample analysis showed that the general population of these fires areas (where the sample was selected) has probability distribution of extreme values of the first type. An analytical expression for the probability distribution function of this general population was received. On the basis of this distribution analysis, a forecast was made concerning risks of the breaking-out and the average recurrence periods of such fires for various values of the burning area. The conducted analysis showed that in 87.5% of cases, in the south of Khabarovsk territory, the largest-area yearly forest fires, with an area of from 50 to 400 km2, will break out with the 1.2 years recurrence interval. In other words, almost every year, with the exception of these rare events when fires with other areas will occur.  It was supposed that the probability distribution of extreme values of the first type can be applied not only to the forest area of Russia’s Khabarovsk territory, but also to other zones in the world with large forest areas.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

The Asymptotic Theory of the Kakwani Class of Poverty Measures

2007 ◽  
Author(s):  
Gane Samb Lo ◽  
Serigne Touba Sall ◽  
Cheikh Tidiane Seck
Keyword(s):  
Asymptotic Theory ◽  
Poverty Measures

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

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