scholarly journals A Comparison of Maximum Likelihood and Expected A Posteriori Estimation for Polychoric Correlation Using Monte Carlo Simulation

2009 ◽  
Vol 8 (1) ◽  
pp. 337-354 ◽  
Author(s):  
Jinsong Chen ◽  
Jaehwa Choi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
A Posteriori ◽  
Polychoric Correlation ◽  
Posteriori Estimation

Performance Study of Time Delay Estimation in a Multi-Path Environment

1997 ◽  
Vol 07 (06) ◽  
pp. 599-606
Author(s):  
Ming Jian ◽  
Alex C. Kot ◽  
Meng H. Er
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Delay ◽  
Maximum Likelihood ◽  
Theoretical Analysis ◽  
Close Agreement ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Performance Study ◽  
Predicted Values

A theoretical analysis of the performance of the maximum likelihood (ML) time delay estimate in a multi-path propagation is proposed. An expression for the probability of anomaly of the ML time delay estimate is obtained. Percentages of anomalous time delay estimates obtained through Monte Carlo simulation are shown to be in close agreement with theoretically predicted values.

Failure rate analysis of jaw crusher using Weibull model

2016 ◽  
Vol 231 (4) ◽  
pp. 760-772 ◽  
Author(s):  
RS Sinha ◽  
AK Mukhopadhyay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Likelihood Estimation ◽  
Jaw Crusher ◽  
Two Parameter

The primary crusher is essential equipment employed for comminuting the mineral in processing plants. Any kind of failure of its components will accordingly hinder the performance of the plant. Therefore, to minimize sudden failures, analysis should be undertaken to improve performance and operational reliability of the crushers and its components. This paper considers the methods for analyzing failure rates of a jaw crusher and its critical components application of a two-parameter Weibull distribution in a mineral processing plant fitted using statistical tests such as goodness of fit and maximum likelihood estimation. Monte Carlo simulation, analysis of variance, and artificial neural network are also applied. Two-parameter Weibull distribution is found to be the best fit distribution using Kolmogorov–Smirnov test. Maximum likelihood estimation method is used to find out the shape and scale parameter of two-parameter Weibull distribution. Monte Carlo simulation generates 40 numbers of shape parameters, scale parameters, and time. Further, 40 numbers of Weibull distribution parameters are evaluated to examine the failure rate, significant difference, and regression coefficient using ANOVA. Artificial neural network with back-propagation algorithm is used to determine R2 and is compared with analysis of variance.

scholarly journals Robustness Test of Selected Estimators of Linear Regression with Autocorrelated Error Term: A Monte-Carlo Simulation Study

2021 ◽  
pp. 1-17
Author(s):  
Rauf Ibrahim Rauf ◽  
Okoli Juliana Ifeyinwa ◽  
Haruna Umar Yahaya
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Linear Regression ◽  
Simulation Study ◽  
Explanatory Variable ◽  
Real Life ◽  
Least Square ◽  
Monte Carlo Simulation Study ◽  
Error Terms

Assumptions in the classical linear regression model include that of lack of autocorrelation of the error terms and the zero covariance between the explanatory variable and the error terms. This study is channeled towards the estimation of the parameters of the linear models for both time series and cross-sectional data when the above two assumptions are violated. The study used the Monte-Carlo simulation method to investigate the performance of six estimators: ordinary least square (OLS), Prais-Winsten (PW), Cochrane-Orcutt (CC), Maximum Likelihood (MLE), Restricted Maximum- Likelihood (RMLE) and the Weighted Least Square (WLS) in estimating the parameters of a single linear model in which the explanatory variable is also correlated with the autoregressive error terms. Using the models’ finite properties(mean square error) to measure the estimators’ performance, the results shows that OLS should be preferred when autocorrelation level is relatively mild (ρ = 0.3) and the PW, CC, RMLE, and MLE estimator will perform better with the presence of any level of AR (1) disturbance between 0.4 to 0.8 level, while WLS shows better performance at 0.9 level of autocorrelation and above. The study thus recommended the application of the various estimators considered to real-life data to affirm the results of this simulation study.

scholarly journals Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling

2020 ◽  
Vol 68 (6) ◽  
pp. 1896-1912
Author(s):  
Yijie Peng ◽  
Michael C. Fu ◽  
Bernd Heidergott ◽  
Henry Lam
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Modeling ◽  
Stochastic Models ◽  
Likelihood Estimation ◽  
Data Driven ◽  
Simulation Based

A Simulation-Based Approach for Calibrating Stochastic Models

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