scholarly journals Inference on reliability of stress-strength model with Peng-Yan extended Weibull distributions

Filomat ◽  
2021 ◽  
Vol 35 (6) ◽  
pp. 1927-1948
Author(s):  
Milan Jovanovic ◽  
Bojana Milosevic ◽  
Marko Obradovic ◽  
Zoran Vidovic
Keyword(s):  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Real Data ◽  
Independent Random Variables ◽  
Likelihood Estimator ◽  
Conjugate Priors ◽  
Asymptotic Confidence Intervals ◽  
Exact Confidence Intervals ◽  
Extended Weibull Distribution ◽  
Interval Estimator

In this paper we estimate R = PfX < Yg when X and Y are independent random variables following the Peng-Yan extended Weibull distribution. We find maximum likelihood estimator of R and its asymptotic distribution. This asymptotic distribution is used to construct asymptotic confidence intervals. In the case of equal shape parameters, we derive the exact confidence intervals, too. A procedure for deriving bootstrap-p confidence intervals is presented. The UMVUE of R and the UMVUE of its variance are derived and also the Bayes point and interval estimator of R for conjugate priors are obtained. Finally, we perform a simulation study in order to compare these estimators and provide a real data example.

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.

Asymptotic Confidence Intervals for the Optimal Cost in Newsboy Problem

1996 ◽  
Vol 46 (3-4) ◽  
pp. 245-252 ◽  
Author(s):  
Manisha Pal
Keyword(s):  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Confidence Limits ◽  
Newsboy Problem ◽  
Optimal Cost ◽  
Demand Distribution ◽  
Ordering Policy ◽  
Asymptotic Confidence Intervals ◽  
Optimal Ordering Policy ◽  
Optimal Ordering

This paper attempts to set approximate confidence limits for tlie expected cost resulting from an estimated optimal ordering policy in the newsboy problem, when the demand distribution is completely unknown. The asymptotic distribution of the estimated cost, the bootstrap and the bootstrap­ t procedures have been used for the purpose.

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.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

scholarly journals Nonparametric estimation of the measure of functional dependence

2021 ◽  
Vol 6 (12) ◽  
pp. 13488-13502
Author(s):  
Qingsong Shan ◽  
◽  
Qianning Liu
Keyword(s):  
Asymptotic Distribution ◽  
Nonparametric Estimation ◽  
Functional Dependence ◽  
Kernel Estimator ◽  
Real Data ◽  
High Accuracy ◽  
Simulation Results

<abstract><p>In this paper, we propose a beta kernel estimator to measure functional dependence (MFD). The MFD not only can measure the strength of linear or monotonic relationships, but it is also suitable for more complicated functional dependence. We derive the asymptotic distribution of the proposed estimator and then use several simulated examples to compare our estimator with the traditional measures. Our simulation results demonstrate that beta kernel provides high accuracy in estimation. A real data example is also given to illustrate one possible application of the new estimator.</p></abstract>

scholarly journals On the Correction of the Asymptotic Distribution of the Likelihood Ratio Statistic If Nuisance Parameters Are Estimated Based on an External Source

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Marianne Jonker ◽  
Aad Van der Vaart
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Likelihood Ratio ◽  
Statistical Models ◽  
Likelihood Ratio Statistic ◽  
External Source ◽  
Nuisance Parameters ◽  
Testing Hypotheses

AbstractIn practice, nuisance parameters in statistical models are often replaced by estimates based on an external source, for instance if estimates were published before or a second dataset is available. Next these estimates are assumed to be known when the parameter of interest is estimated, a hypothesis is tested or confidence intervals are constructed. By this assumption, the level of the test is, in general, higher than supposed and the coverage of the confidence interval is too low. In this article, we derive the asymptotic distribution of the likelihood ratio statistic if the nuisance parameters are estimated based on a dataset that is independent of the data used for estimating the parameter of interest. This distribution can be used for correctly testing hypotheses and constructing confidence intervals. Four theoretical and practical examples are given as illustration.

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