stochastic structure
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Author(s):  
Evgeniya G. Kablukova ◽  
Sergei M. Prigarin

Abstract The paper is focused on construction of computational models of stratus clouds using remote sensing data and on Monte Carlo analysis of optical radiation transfer peculiarities caused by stochastic structure of clouds.



2021 ◽  
Author(s):  
Konstantinos Papoulakos ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Dimosthenis Tsaknias ◽  
Demetris Koutsoyiannis

<p>During the last decades, scientific research in the field of flood risk management has provided new insights and strong computational tools towards the deeper understanding of the fundamental probabilistic and stochastic behaviour that characterizes such natural hazards. Flood hazards are controlled by hydrometeorological processes and their inherent uncertainties. Historically, a high percentage of flood disasters worldwide are inaccurately or ineffectively reported regarding the aggregated number of the affected people, economic losses and generated flood insurance claims. In this respect, the recently published National Flood Insurance Program (NFIP) data by the Federal Emergency Management Agency (FEMA), including more than two million claims records dating back to 1978 and more than 47 million policy records for transactions, may provide new insights into flood impacts. The aim of this research is to process the actual insurance data derived from this database, in order to detect the underlying patterns and investigate its stochastic structure, paving the way for the development of more accurate flood risk assessment and modelling strategies.</p>



2021 ◽  
Author(s):  
Olianna Akoumianaki ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Emmanouil Varouchakis ◽  
Demetris Koutsoyiannis

<p>In the last few years, the island of Crete (Greece - Eastern Mediterranean) has been affected by extreme events. In recent decades, hydrometeorological processes in the island of Crete are monitored by an extensive network of meteorological stations. Here we stochastically analyze the spatial stochastic structure of precipitation in the island by employing sophisticated statistical tools, as well as by analyzing a large database of daily precipitation records. We investigate fifty-eight rainfall stations scattered in the four prefectures of Crete, for the years 1974-2020. Descriptive statistical analysis of precipitation examines several temporal properties in the data, while correlation analysis of precipitation variability provides relations between stations and regions for spatial patterns identification. This work also investigates the precipitation variability by employing statistical tools such as the autocorrelation, autoregressive (seasonal) analysis, probability distribution function fitting, and climacogram calculation, i.e. variance of the averaged process vs. spatial and temporal scales, to identify statistical properties, temporal dependencies, potential similarities in the dependence structure and marginal probability distribution.</p>



2020 ◽  
Vol 58 (5) ◽  
pp. 3040-3067
Author(s):  
Junlong Lyu ◽  
Zhongjian Wang ◽  
Jack Xin ◽  
Zhiwen Zhang




Econometrics ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 41-52
Author(s):  
Stanisław Jaworski


2019 ◽  
Vol 105 ◽  
pp. 930-944 ◽  
Author(s):  
Y.T. Sun ◽  
L.F. Luo ◽  
Q. Zhang ◽  
X.R. Qin


2019 ◽  
Vol 52 (4) ◽  
pp. 477-504
Author(s):  
Karl Brunner ◽  
Alex Cukierman ◽  
Allan H. Meltzer

When changes occur, people do not know how long they will persist. Using a simple stochastic structure that incorporates temporary and permanent changes in an augmented IS-LM model, we show that rising prices and rising unemployment – stagflation – is likely to follow a large permanent reduction to productivity. All markets clear and all expectations are rational. People learn gradually the permanent values which the economy will reach following a permanent shock and gradually adjust anticipations. In our model, optimally perceived permanent values take the form of a Koyck lag of past observations.



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