scholarly journals Extreme statistics of non-intersecting Brownian paths

2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Gia Bao Nguyen ◽  
Daniel Remenik
Keyword(s):  
Extreme Statistics

Short term extreme statistics of local ice loads on ship hulls

2012 ◽  
Vol 82 ◽  
pp. 130-143 ◽  
Author(s):  
A. Suyuthi ◽  
B.J. Leira ◽  
K. Riska
Keyword(s):  
Short Term ◽  
Extreme Statistics ◽  
Ship Hulls ◽  
Ice Loads

Extreme Statistics of Storm Surges in the Baltic Sea

Oceanology ◽  
2017 ◽  
Vol 57 (6) ◽  
pp. 772-783 ◽  
Author(s):  
E. A. Kulikov ◽  
I. P. Medvedev
Keyword(s):  
Baltic Sea ◽  
Storm Surges ◽  
The Baltic Sea ◽  
Extreme Statistics ◽  
The Baltic

scholarly journals Exceedance frequency of appearance of the extreme internal waves in the World Ocean

2018 ◽  
Vol 25 (3) ◽  
pp. 511-519 ◽  
Author(s):  
Tatyana Talipova ◽  
Efim Pelinovsky ◽  
Oxana Kurkina ◽  
Ayrat Giniyatullin ◽  
Andrey Kurkin
Keyword(s):  
Internal Waves ◽  
Yellow Sea ◽  
World Ocean ◽  
The Yellow Sea ◽  
Exceedance Probability ◽  
Extreme Statistics ◽  
Statistical Estimates ◽  
Northwestern Shelf ◽  
The World ◽  
Egyptian Coast

Abstract. Statistical estimates of internal waves in different regions of the World Ocean are discussed. It is found that the observed exceedance probability of large-amplitude internal waves in most cases can be described by the Poisson law, which is one of the typical laws of extreme statistics. Detailed analysis of the statistical properties of internal waves in several regions of the World Ocean has been performed: tropical part of the Atlantic Ocean, northwestern shelf of Australia, the Mediterranean Sea near the Egyptian coast, and the Yellow Sea.

scholarly journals Development of a Pressure–Precipitation Transmitter

2019 ◽  
Vol 58 (11) ◽  
pp. 2453-2468
Author(s):  
Masaru Inatsu ◽  
Tamaki Suematsu ◽  
Yuta Tamaki ◽  
Naoto Nakano ◽  
Kao Mizushima ◽  
...  
Keyword(s):  
Regression Model ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Precipitation Pattern ◽  
Multilevel Regression ◽  
Extreme Statistics ◽  
Spatial Continuity ◽  
Daily Precipitation Data ◽  
Multilevel Regression Model

AbstractA novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.

Asymptotic distributions of extremes of extremal Markov sequences

1994 ◽  
Vol 31 (01) ◽  
pp. 256-261
Author(s):  
S. R. Adke ◽  
C. Chandran
Keyword(s):  
Distribution Function ◽  
Random Variables ◽  
Asymptotic Distributions ◽  
Arbitrary Distribution ◽  
Extreme Statistics ◽  
Common Distribution ◽  
Common Distribution Function ◽  
The Common

Let {ξ n , n ≧1} be a sequence of independent real random variables, F denote the common distribution function of identically distributed random variables ξ n , n ≧1 and let ξ 1 have an arbitrary distribution. Define Xn+ 1 = k max(Xn, ξ n +1), Yn + 1 = max(Yn, ξ n +1) – c, Un +1 = l min(Un, ξ n +1), Vn+ 1 = min(Vn, ξ n +1) + c, n ≧ 1, 0 < k < 1, l > 1, 0 < c < ∞, and X 1 = Υ 1 = U 1 = V 1 = ξ 1. We establish conditions under which the limit law of max(X 1, · ··, Xn ) coincides with that of max(ξ 2, · ··, ξ n+ 1) when both are appropriately normed. A similar exercise is carried out for the extreme statistics max(Y 1, · ··, Yn ), min(U 1,· ··, Un ) and min(V 1, · ··, Vn ).

Semi-Parametric Statistical Model for Extreme Value Statistical Models and Application in Automatic Control

2014 ◽  
Vol 680 ◽  
pp. 455-458
Author(s):  
Yu Han
Keyword(s):  
Parameter Estimation ◽  
Automatic Control ◽  
Statistical Models ◽  
Estimation Method ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Vector Model ◽  
Extreme Statistics ◽  
The Impact

The frequency that extreme events appear in the life is low,but once it appears,the impact will be significant; many scholars have conducted in depth research and found that statistical theory of extreme value. The theory of extreme statistics plays a more and more important role in many fields such as automatic control, assembly line etc. This paper,makes an in-depth research towards the characteristics and parameter estimation of the extreme value statistical models,as well as the application,mainly analyzes the Bayes parameter estimation method of extreme value distribution,the extreme value distribution theory and Copula function random vector model.

scholarly journals To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

2009 ◽  
Vol 60 (1) ◽  
pp. 87-95 ◽  
Author(s):  
K. Schaarup-Jensen ◽  
M. R. Rasmussen ◽  
S. Thorndahl
Keyword(s):  
Extreme Events ◽  
Extreme Event ◽  
Rain Gauge ◽  
Water Levels ◽  
Rainfall Series ◽  
Rainfall Time Series ◽  
Extreme Statistics ◽  
Maximum Water ◽  
Present Case Study

In urban drainage modelling long term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties with regards to long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO volumes. Traditionally, long term rainfall series, from a local rain gauge, are unavailable. In the present case study, however, long and local rain series are available. 2 rainfall gauges have recorded events for approximately 9 years at 2 locations within the catchment. Beside these 2 gauges another 7 gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity can be handled, e.g. by introducing an “averaging procedure” based on the variability within the set of statistics. All simulations are performed by means of the MOUSE LTS model.

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