Estimation of the optimal design of a nonlinear parametric regression problem via Monte Carlo experiments

2013 ◽  
Vol 59 ◽  
pp. 1-12 ◽  
Author(s):  
Ida Hertel ◽  
Michael Kohler
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Regression Problem ◽  
Parametric Regression ◽  
Monte Carlo Experiments

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.

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

scholarly journals Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 258
Author(s):  
Zhihang Xu ◽  
Qifeng Liao
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Information Gain ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Likelihood Functions ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Bayesian Monte Carlo ◽  
Expected Information Gain

Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.

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