Estimation From Censored, Interval Data of the Median Breaking Point of Polyethylene Subjected to Stress-Cracking Tests: A Monte Carlo Study

1975 ◽  
Vol 42 (3) ◽  
pp. 607-612 ◽  
Author(s):  
L. Denby ◽  
E. B. Fowlkes ◽  
R. J. Roe
Keyword(s):  
Monte Carlo ◽  
Lognormal Distribution ◽  
Monte Carlo Study ◽  
Interval Data ◽  
Efficient Estimation ◽  
Breaking Point ◽  
Mean Square ◽  
Stress Cracking ◽  
Environmental Stress Cracking ◽  
Bias Variance

A Monte Carlo study has been carried out in order to study the properties of various alternative estimators for median breaking point of polyethylene specimens subjected to an environmental stress cracking test. Tables of bias, variance, and mean square error have been derived for different sample sizes, interval sizes, and levels of censoring data with the lognormal distribution as a model. These tables will aid in the design of experiments for efficient estimation of median breaking point.

scholarly journals On Smoothed MWSD Estimation of Mixing Proportion

2021 ◽  
pp. 971-982
Author(s):  
Satish Konda ◽  
Mehra, K.L. ◽  
Ramakrishnaiah Y.S.
Keyword(s):  
Monte Carlo ◽  
Asymptotic Normality ◽  
Minimum Mean Square Error ◽  
Monte Carlo Study ◽  
Mean Square ◽  
Random Samples ◽  
Two Component ◽  
Mixing Proportion ◽  
Mean Square Error Criterion ◽  
Mean Square Errors

The problem considered in the present paper is estimation of mixing proportions of mixtures of two (known) distributions by using the minimum weighted square distance (MWSD) method. The two classes of smoothed and unsmoothed parametric estimators of mixing proportion proposed in a sense of MWSD due to Wolfowitz(1953) in a mixture model F(x)=p (x)+(1-p) (x) based on three independent and identically distributed random samples of sizes n and , =1,2 from the mixture and two component populations. Comparisons are made based on their derived mean square errors (MSE). The superiority of smoothed estimator over unsmoothed one is established theoretically and also conducting Monte-Carlo study in sense of minimum mean square error criterion. Large sample properties such as rates of a.s. convergence and asymptotic normality of these estimators are also established. The results thus established here are completely new in the literature.

scholarly journals Monte Carlo comparison of estimation methods for the two-parameter lognormal distribution

2019 ◽  
Vol 254 ◽  
pp. 08002
Author(s):  
Ivana Pobočíková ◽  
Zuzana Sedliačková ◽  
Mária Michalková ◽  
Lenka Kuchariková
Keyword(s):  
Monte Carlo Simulation ◽  
Tensile Strength ◽  
Monte Carlo ◽  
Ultimate Tensile Strength ◽  
Lognormal Distribution ◽  
Real Data ◽  
Estimation Methods ◽  
Mean Square ◽  
Data Set ◽  
Two Parameter

In the paper we compare performance of estimation methods for the two-parameter lognormal distribution via the Monte Carlo simulation. The comparison of performances is made with respect to their biases, variances, root mean square error. The methods are applied on real data set representing experimentally obtained values of ultimate tensile strength of material.

scholarly journals A Strategy for Using Bias and RMSE as Outcomes in Monte Carlo Studies in Statistics

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Michael Harwell
Keyword(s):  
Monte Carlo ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Monte Carlo Study ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Mean Square ◽  
Type Ii Error ◽  
Monte Carlo Studies

To help ensure important patterns of bias and accuracy are detected in Monte Carlo studies in statistics this paper proposes conditioning bias and root mean square error (RMSE) measures on estimated Type I and Type II error rates. A small Monte Carlo study is used to illustrate this argument.

scholarly journals Virtually increased acceptance angle for efficient estimation of spatially resolved reflectance in the subdiffusive regime: a Monte Carlo study

2017 ◽  
Vol 8 (11) ◽  
pp. 4872 ◽  
Author(s):  
Matic Ivančič ◽  
Peter Naglič ◽  
Franjo Pernuš ◽  
Boštjan Likar ◽  
Miran Bürmen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Efficient Estimation ◽  
Acceptance Angle ◽  
Spatially Resolved

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

Detecting Between-Groups Heteroscedasticity in MMR: A Monte Carlo Study

2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study
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