Monte-Carlo statistical analysis of non-linear flying attitude

1995 ◽  
Vol 31 (6) ◽  
pp. 2988-2990 ◽  
Author(s):  
Yiao-Tee Hsia ◽  
Ciuter Chang
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Non Linear ◽  
Flying Attitude

scholarly journals Monte Carlo methods for linear and non-linear Poisson-Boltzmann equation

2015 ◽  
Vol 48 ◽  
pp. 420-446 ◽  
Author(s):  
Mireille Bossy ◽  
Nicolas Champagnat ◽  
Hélène Leman ◽  
Sylvain Maire ◽  
Laurent Violeau ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Boltzmann Equation ◽  
Monte Carlo Methods ◽  
Non Linear ◽  
Poisson Boltzmann ◽  
Poisson Boltzmann Equation

MONTE CARLO METHODS IN FUZZY NON-LINEAR REGRESSION

2008 ◽  
Vol 04 (02) ◽  
pp. 123-141 ◽  
Author(s):  
AREEG ABDALLA ◽  
JAMES BUCKLEY
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Linear Regression ◽  
Fuzzy Numbers ◽  
Random Number ◽  
Random Number Generator ◽  
Search Space ◽  
Triangular Fuzzy Numbers ◽  
Non Linear ◽  
Regression Problems

We apply our new fuzzy Monte Carlo method to certain fuzzy non-linear regression problems to estimate the best solution. The best solution is a vector of triangular fuzzy numbers, for the fuzzy coefficients in the model, which minimizes an error measure. We use a quasi-random number generator to produce random sequences of these fuzzy vectors which uniformly fill the search space. We consider example problems to show that this Monte Carlo method obtains solutions comparable to those obtained by an evolutionary algorithm.

scholarly journals Genz and Mendell-Elston Estimation of the High-Dimensional Multivariate Normal Distribution

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 296
Author(s):  
Lucy Blondell ◽  
Mark Z. Kos ◽  
John Blangero ◽  
Harald H. H. Göring
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Execution Time ◽  
Absolute Error ◽  
High Dimensional ◽  
Multivariate Normal ◽  
Multinomial Data ◽  
Efficient Performance ◽  
Simulation Based ◽  
Scale Characteristics

Statistical analysis of multinomial data in complex datasets often requires estimation of the multivariate normal (mvn) distribution for models in which the dimensionality can easily reach 10–1000 and higher. Few algorithms for estimating the mvn distribution can offer robust and efficient performance over such a range of dimensions. We report a simulation-based comparison of two algorithms for the mvn that are widely used in statistical genetic applications. The venerable Mendell-Elston approximation is fast but execution time increases rapidly with the number of dimensions, estimates are generally biased, and an error bound is lacking. The correlation between variables significantly affects absolute error but not overall execution time. The Monte Carlo-based approach described by Genz returns unbiased and error-bounded estimates, but execution time is more sensitive to the correlation between variables. For ultra-high-dimensional problems, however, the Genz algorithm exhibits better scale characteristics and greater time-weighted efficiency of estimation.

Recovering cointegration via wavelets in the presence of non-linear patterns

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jorge Martínez Compains ◽  
Ignacio Rodríguez Carreño ◽  
Ramazan Gençay ◽  
Tommaso Trani ◽  
Daniel Ramos Vilardell
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Gross National Product ◽  
Structural Breaks ◽  
Low Frequency ◽  
Cointegration Test ◽  
Seasonal Adjustment ◽  
Traditional Use ◽  
Monte Carlo Experiments ◽  
Non Linear

Abstract Johansen’s Cointegration Test (JCT) performs remarkably well in finding stable bivariate cointegration relationships. Nonetheless, the JCT is not necessarily designed to detect such relationships in presence of non-linear patterns such as structural breaks or cycles that fall in the low frequency portion of the spectrum. Seasonal adjustment procedures might not detect such non-linear patterns, and thus, we expose the difficulty in identifying cointegrating relations under the traditional use of JCT. Within several Monte Carlo experiments, we show that wavelets can empower more the JCT framework than the traditional seasonal adjustment methodologies, allowing for identification of hidden cointegrating relationships. Moreover, we confirm these results using seasonally adjusted time series as US consumption and income, gross national product (GNP) and money supply M1 and GNP and M2.

Non-linear Monte Carlo Modelling Requirements

2020 ◽  
pp. 221-232
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Monte Carlo ◽  
Dynamic Risk ◽  
Calibration Methods ◽  
Non Linear ◽  
Project Level

The purpose of this chapter is to finalize requirements to undertake non-linear Monte Carlo modelling. Besides mathematical aspects (Chapter 13), additional requirements to identification and addressing of risk interactions should be put forward. All relevant instances of risk interactions should be identified. Identification of ‘chronic’ project system issues that serve as additional causes of risks is required to pin down internal risk amplifications. Identification of cross-risk interactions can be undertaken by visualization of dynamic risk patterns (cross-risk interaction mapping). Principles of risk interaction calibration are introduced keeping in mind two challenges. First, a calibration of aggregated risk interactions at the project level. Second, evaluation of individual instances of risk interactions based on the overall calibration. Methods to address identified risk interactions are discussed. In the case of intra-risk interactions, the ‘chronic’ issues should be addressed. In the case of cross-risk interactions, the dynamic risk patterns should be disrupted.

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