APPROXIMATE ESTIMATION METHOD FOR PROBABILITY DISTRIBUTION OF FIRST-MODE INELASTIC RESPONSE OF MULTI-STORY FRAMES

2019 ◽  
Vol 84 (764) ◽  
pp. 1315-1323
Author(s):  
Taishi FURUKAWA ◽  
Yasuhiro MORI
Keyword(s):  
Probability Distribution ◽  
Estimation Method ◽  
Inelastic Response ◽  
Approximate Estimation

Research on the Distributed Characteristics of the Delay for Network Coding Based Real-Time Multicast in MANETs

2014 ◽  
Vol 530-531 ◽  
pp. 768-772
Author(s):  
Guo Ping Tan ◽  
Lin Feng Tan ◽  
Lei Cao ◽  
Mei Yan Ju
Keyword(s):  
Maximum Likelihood ◽  
Probability Distribution ◽  
Network Coding ◽  
Real Time ◽  
Lognormal Distribution ◽  
Ad Hoc ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Actual Distribution ◽  
Delay Distribution

For the study of the applications of partial network coding based real-time multicast protocol (PNCRM) in Mobile Ad hoc networks, the researches should be developed in the probability distribution of delay. In this paper, NS2 is used to obtain the delay of data packets through simulations. Because the delay does not obey the strict normal distribution, the maximum likelihood estimate method based on the lognormal distribution is used to process the data. Using MATLAB to obtain the actual distribution of the natural logarithm of delay, then drawing the delay distribution with the maximum likelihood estimation method based on the lognormal distribution, the conclusion that the distributions obtained by the above mentioned methods are basically consistent can be obtained. So the delay distribution of PNCRM meets the lognormal distribution and the characteristic of delay probability distribution can be estimated.

Evaluation of simple quantile estimation functions for modeling forest diameter distributions in even-aged stands of interior Douglas-fir

1993 ◽  
Vol 23 (11) ◽  
pp. 2376-2382 ◽  
Author(s):  
James D. Newberry ◽  
James A. Moore ◽  
Lianjun Zhang
Keyword(s):  
Probability Distribution ◽  
Order Statistics ◽  
Estimation Method ◽  
Douglas Fir ◽  
Quantile Estimation ◽  
Distribution Free ◽  
Inland Northwest ◽  
Distribution Parameters ◽  
Solving Equations ◽  
Diameter Distributions

The method of percentiles usually involves simultaneously solving equations for probability distribution parameters as functions of sample-based estimates of the appropriate quantiles. Eight simple distribution-free methods for estimating quantiles from sample-based order statistics were evaluated empirically using even-aged Douglas-fir (Pseudotsugamenziesii var. glauca (Beissn.) Franco) diameter distributions from the Inland Northwest. Two methods, calculated by weighting adjacent order statistics, consistently gave the best results for both the Weibull and Johnson's SB distributions. Certain distributional shapes were also evaluated to determine if they influenced the quantile estimation method. Although some influence was detected, the best methods were usually best across all categories.

scholarly journals A Parameter Selection Method for Wind Turbine Health Management through SCADA Data

2017 ◽  
Author(s):  
Mian Du ◽  
Jun Yi ◽  
Peyman Mazidi ◽  
Lin Cheng ◽  
Jianbo Guo
Keyword(s):  
Probability Distribution ◽  
Mutual Information ◽  
Wind Turbine ◽  
Health Management ◽  
Estimation Method ◽  
Failure Detection ◽  
Health Condition ◽  
Parameter Selection ◽  
Machine Learning Techniques ◽  
Physical Knowledge

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

scholarly journals Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Mahmood Ul Hassan ◽  
Omar Hayat ◽  
Zahra Noreen
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Direct Method ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Pearson Type

AbstractAt-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.

Estimating the distribution of a stochastic sum of IID random variables

2020 ◽  
Vol 70 (3) ◽  
pp. 759-774
Author(s):  
Viktor Witkovský ◽  
Gejza Wimmer ◽  
Tomas Duby
Keyword(s):  
Probability Distribution ◽  
Heavy Tails ◽  
Expert Knowledge ◽  
Parametric Method ◽  
Estimation Method ◽  
Random Variables ◽  
Parametric Estimation ◽  
Empirical Characteristic Function ◽  
Parametric Modelling ◽  
Non Parametric

AbstractSuggested is a non-parametric method and algorithm for estimating the probability distribution of a stochastic sum of independent identically distributed continuous random variables, based on combining and numerically inverting the associated empirical characteristic function (CF) derived from the observed data. This is motivated by classical problems in financial risk management, actuarial science, and hydrological modelling. This approach can be naturally generalized to more complex semi-parametric modelling and estimating approaches, e.g., by incorporating the generalized Pareto distribution fit for modelling heavy tails of the considered continuous random variables, or by considering the weighted mixture of the parametric CFs (used to incorporate the expert knowledge) and the empirical CFs (used to incorporate the knowledge based on the observed or historical data). The suggested numerical approach is based on combination of the Gil-Pelaez inversion formulae for deriving the probability distribution (PDF and CDF) from the associated CF and the trapezoidal quadrature rule used for the required numerical integration. The presented non-parametric estimation method is related to the bootstrap estimation approach, and thus, it shares similar properties. Applicability of the proposed estimation procedure is illustrated by estimating the aggregate loss distribution from the well-known Danish fire losses data.

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