Optimal Design via Curve Fitting of Monte Carlo Experiments

1995 ◽  
Vol 90 (432) ◽  
pp. 1322 ◽  
Author(s):  
Peter Muller ◽  
Giovanni Parmigiani
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Curve Fitting ◽  
Monte Carlo Experiments

Optimal Design via Curve Fitting of Monte Carlo Experiments

1995 ◽  
Vol 90 (432) ◽  
pp. 1322-1330 ◽  
Author(s):  
Peter Müller ◽  
Giovanni Parmigiani
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Curve Fitting ◽  
Monte Carlo Experiments

scholarly journals Statistical Tests of Symbolic Dynamics

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 817
Author(s):  
Fernando López ◽  
Mariano Matilla-García ◽  
Jesús Mur ◽  
Manuel Ruiz Marín
Keyword(s):  
Monte Carlo ◽  
Symbolic Dynamics ◽  
Null Hypothesis ◽  
Statistical Tests ◽  
General Applicability ◽  
Monte Carlo Experiments ◽  
The Mean ◽  
Theoretical Application ◽  
New Statistics ◽  
General Method

A novel general method for constructing nonparametric hypotheses tests based on the field of symbolic analysis is introduced in this paper. Several existing tests based on symbolic entropy that have been used for testing central hypotheses in several branches of science (particularly in economics and statistics) are particular cases of this general approach. This family of symbolic tests uses few assumptions, which increases the general applicability of any symbolic-based test. Additionally, as a theoretical application of this method, we construct and put forward four new statistics to test for the null hypothesis of spatiotemporal independence. There are very few tests in the specialized literature in this regard. The new tests were evaluated with the mean of several Monte Carlo experiments. The results highlight the outstanding performance of the proposed test.

Novel use of the Monte-Carlo methods to visualize singularity configurations in serial manipulators

2021 ◽  
Vol 15 (2) ◽  
pp. 7948-7963
Author(s):  
Mohamed Aboelnasr ◽  
Hussein M Bahaa ◽  
Ossama Mokhiamar
Keyword(s):  
Monte Carlo ◽  
Numerical Methods ◽  
Curve Fitting ◽  
Jacobian Matrix ◽  
Forward Kinematics ◽  
Graphical Methods ◽  
Kinematic Singularity ◽  
Serial Manipulators ◽  
Serial Robots ◽  
Contour Plots

This paper analyses the problem of the kinematic singularity of 6 DOF serial robots by extending the use of Monte-Carlo numerical methods to visualize singularity configurations. To achieve this goal, first, forward kinematics and D-H parameters have been derived for the manipulator. Second, the derived equations are used to generate and visualize a workspace that gives a good intuition of the motion shape of the manipulator. Third, the Jacobian matrix is computed using graphical methods, aiming to locate positions that cause singularity. Finally, the data obtained are processed in order to visualize the singularity and to design a trajectory free of singularity. MATLAB robotics toolbox, Symbolic toolbox, and curve fitting toolbox are the MATLAB toolboxes used in the calculations. The results of the surface and contour plots of the determinate of the Jacobian matrix behavior lead to design a manipulator’s trajectory free of singularity and show the parameters that affect the manipulator’s singularity and its behavior in the workspace.

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.

scholarly journals Some Monte Carlo experiments in computing multiple integrals

1956 ◽  
Vol 10 (53) ◽  
pp. 1-1 ◽  
Author(s):  
P. Davis ◽  
P. Rabinowitz
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Experiments ◽  
Multiple Integrals
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