scholarly journals Classical and Bayesian Inference of Conditional Stress-Strength Model under Kumaraswamy Distribution

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fathy H. Riad ◽  
Mohammad Mehdi Saber ◽  
Mehrdad Taghipour ◽  
M. M. Abd El-Raouf
Keyword(s):  
Bayesian Inference ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Strength Model ◽  
Likelihood Estimator ◽  
Kumaraswamy Distribution ◽  
Strength Models

Stress-strength models have been frequently studied in recent years. An applicable extension of these models is conditional stress-strength models. The maximum likelihood estimator of conditional stress-strength models, asymptotic distribution of this estimator, and its confidence intervals are obtained for Kumaraswamy distribution. In addition, Bayesian estimation and bootstrap method are applied to the model.

scholarly journals A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model

2019 ◽  
Vol 9 (15) ◽  
pp. 3120
Author(s):  
Sandro Amador ◽  
Mahmoud El-Kafafy ◽  
Álvaro Cunha ◽  
Rune Brincker
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Maximum Likelihood Estimator ◽  
Confidence Intervals ◽  
Modal Parameters ◽  
Parameter Estimates ◽  
Modal Parameter ◽  
Likelihood Estimator ◽  
Pole Residue ◽  
Modal Model

Recently, a lot of efforts have been devoted to developing more precise Modal Parameter Estimation (MPE) techniques. This is explained by the necessity in civil, mechanical and aerospace engineering of obtaining accurate estimates for the modal parameters of the tested structures, as well as of determining reliable confidence intervals for these estimates. The Non-linear Least Squares (NLS) identification techniques based on Maximum Likelihood (ML) have been increasingly used in modal analysis to improve precision of estimates provided by the Least Squares (LS) based estimators when they are not accurate enough. Apart from providing more accurate estimates, the main advantage of the ML estimators, with regard to their LS counterparts, is that they allow for taking into account not only the measured Frequency Response Functions (FRFs) but also the noise information during the parametric identification process and, therefore, provide the modal parameters estimates together with their uncertainties bounds. In this paper, a new derivation of a Maximum Likelihood Estimator formulated in Pole-residue Modal Model (MLE-PMM) is presented. The proposed formulation is meant to be used in combination with the Least Squares Frequency Domain (LSCF) to improve the precision of the modal parameter estimates and compute their confidence intervals. Aiming at demonstrating the efficiency of the proposed approach, it is applied to two simulated examples in the final part of the paper.

scholarly journals Parametric Bootstrapping Predictive Estimator for Logistic Regression

2019 ◽  
pp. 1-15
Author(s):  
Kunio Takezawa
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Cross Validation ◽  
Bootstrap Method ◽  
Maximum Likelihood Estimators ◽  
Parametric Bootstrap ◽  
Likelihood Estimator ◽  
Parametric Bootstrapping

This paper proposes a method for constructing a predictive estimator for logistic regression. We make a provisional assumption that the predictive estimator is given by multiplying the maximum likelihood estimators by constants, which are estimated using a parametric bootstrap method. The relative merits of the maximum likelihood estimator and the predictive estimator produced by this method are determined by cross-validation. The results show that the predictiveestimators derived by this method lead to a smaller deviance than that obtained by the maximum likelihood estimator in many instances.

scholarly journals Estimation of P{X < Y} for gamma exponential model

2014 ◽  
Vol 24 (2) ◽  
pp. 283-291 ◽  
Author(s):  
Milan Jovanovic ◽  
Vesna Rajic
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Exponential Distribution ◽  
Asymptotic Distribution ◽  
Simulation Study ◽  
Random Variables ◽  
Exponential Model ◽  
Independent Random Variables ◽  
Likelihood Estimator

In this paper, we estimate probability P{X < Y} when X and Y are two independent random variables from gamma and exponential distribution, respectively. We obtain maximum likelihood estimator and its asymptotic distribution. We perform some simulation study.

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