Estimation of lifetime distribution parameters with general progressive censoring from Imprecise data

2021 ◽  
Vol 13 (4) ◽  
pp. 807-818
Author(s):  
Abbas Pak ◽  
Mohammad Reza Mahmoudi
Keyword(s):  
Lifetime Distribution ◽  
Imprecise Data ◽  
Progressive Censoring ◽  
Distribution Parameters

A New Technique for Optimization of Product Acceptance Process in Terms of Misclassification Probability

2018 ◽  
pp. 1-32
Author(s):  
Nicholas A. Nechval ◽  
Konstantin N. Nechval
Keyword(s):  
Weibull Distribution ◽  
Optimization Technique ◽  
Parametric Uncertainty ◽  
Lifetime Distribution ◽  
Statistical Quality Control ◽  
Misclassification Probability ◽  
Distribution Parameters ◽  
A New Technique ◽  
Two Parameter

A product acceptance process is an inspecting one in statistical quality control or reliability tests, which are used to make decisions about accepting or rejecting lots of products to be submitted. This process is important for industrial and business purposes of quality management. To determine the optimal parameters of the product acceptance process under parametric uncertainty of underlying lifetime models (in terms of misclassification probability), a new optimization technique is proposed. The most popular lifetime distribution used in the field of product acceptance is a two-parameter Weibull distribution, with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Therefore, the situations are also considered when both Weibull distribution parameters are unknown. An illustrative numerical example is given.

scholarly journals Bayesian and Maximum Likelihood Estimation for the Weibull Generalized Exponential Distribution Parameters Using Progressive Censoring Schemes

2018 ◽  
pp. 853-868 ◽  
Author(s):  
Ehab Mohamed Almetwally ◽  
Hisham Mohamed Almongy ◽  
Amaal El sayed Mubarak
Keyword(s):  
Maximum Likelihood ◽  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Real Data ◽  
Likelihood Method ◽  
Generalized Exponential Distribution ◽  
Progressive Censoring ◽  
Linex Loss ◽  
Distribution Parameters ◽  
Optimal Censoring

In this paper we consider the estimation of the Weibull Generalized Exponential Distribution (WGED) Parameters with Progressive Censoring Schemes. In order to obtain the optimal censoring scheme for WGED, more than one method of estimation was used to reach a better scheme with the best method of estimation. The maximum likelihood method and the method of Bayesian estimation for (square error and Linex) loss function have been used. Monte carlo simulation is used for comparison between the two methods of estimation under censoring schemes. To show how the schemes work in practice; we analyze a strength data for single carbon fibers as a case of real data.

scholarly journals New Distribution for Fitting Discrete Data: The Poisson-Gold Distribution and Its Statistical Properties

2021 ◽  
Vol 50 (4) ◽  
pp. 19-35
Author(s):  
Ahmad Hanandeh ◽  
Amjad D. Al-Nasser
Keyword(s):  
Method Of Moments ◽  
Real Data ◽  
Moment Generating Function ◽  
Lifetime Distribution ◽  
Superior Performance ◽  
Lifetime Distributions ◽  
Practical Applications ◽  
Mathematical Properties ◽  
Distribution Parameters ◽  
New Distribution

Motivated mainly by lifetime issues, a new lifetime distribution coined ``Discrete Poisson-Gold distribution'' is introduced in this paper. Different structural properties of the new distribution are derived including moment generating function and the $r^{th}$ moment and others are presented. In addition, we discussed various important mathematical properties of the new distribution including estimation procedures for estimating the distribution parameters using the maximum likelihood and method of moments. The usefulness and credibility of the distribution are illustrated by means of two real-data applications to show its superior performance over some other well-known lifetime distributions and to prove its versatility in practical applications.

scholarly journals Estimating the parameters of lifetime distributions under progressively Type-II censoring from fuzzy data

2016 ◽  
Vol 12 (1) ◽  
pp. 41-53 ◽  
Author(s):  
N. B. Khoolenjani ◽  
O. Chatrabgoun
Keyword(s):  
Fuzzy Numbers ◽  
Lifetime Distribution ◽  
Maximum Likelihood Estimates ◽  
Estimation Methods ◽  
Type Ii ◽  
Lifetime Distributions ◽  
Type Ii Censoring ◽  
Lifetime Analysis ◽  
Distribution Parameters ◽  
Censoring Scheme

Abstract The problem of estimating lifetime distribution parameters under progressively Type-II censoring originated in the context of reliability. But traditionally it is assumed that the available data from this censoring scheme are performed in exact numbers. However, some collected lifetime data might be imprecise and are represented in the form of fuzzy numbers. Thus, it is necessary to generalize classical statistical estimation methods for real numbers to fuzzy numbers. This paper deals with the estimation of lifetime distribution parameters under progressively Type-II censoring scheme when the lifetime observations are reported by means of fuzzy numbers. A new method is proposed to determine the maximum likelihood estimates of the parameters of interest. The methodology is illustrated with two popular models in lifetime analysis, the Rayleigh and Lognormal lifetime distributions.

scholarly journals Reliability sensitivity analysis of coherent systems based on survival signature

2018 ◽  
Vol 232 (6) ◽  
pp. 627-634 ◽  
Author(s):  
Xianzhen Huang ◽  
Frank PA Coolen
Keyword(s):  
Sensitivity Analysis ◽  
Lifetime Distribution ◽  
Relative Importance ◽  
Rank Distribution ◽  
Coherent Systems ◽  
Reliability Improvement ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Distribution Parameters ◽  
Overall Reliability

The reliability sensitivity can be used to rank distribution parameters of system components concerning their impacts on the system’s reliability. Such information is essential to purposes such as component prioritization, reliability improvement, and risk reduction of a system. In this article, we present an efficient method for reliability sensitivity analysis of coherent systems using survival signature. The survival signature is applied to calculate the reliability of coherent systems. The reliability importance of components is derived analytically to evaluate the relative importance of the component with respect to the overall reliability of the system. The closed-form formula for the reliability sensitivity of the system with respect to component’s distribution parameters is derived from the derivative of lifetime distribution of a component to further investigate the impacts of the distribution parameters on the system’s reliability. The effectiveness and feasibility of the proposed approaches are demonstrated with two numerical examples.

Statistical criteria of stellar population types

1966 ◽  
Vol 24 ◽  
pp. 101-110
Author(s):  
W. Iwanowska
Keyword(s):  
Stellar Population ◽  
Chemical Properties ◽  
Spectrophotometric Study ◽  
Physical And Chemical Properties ◽  
Population Type ◽  
The Galaxy ◽  
Distribution Parameters ◽  
Physical And Chemical ◽  
Statistical Criteria ◽  
And Chemical Properties

In connection with the spectrophotometric study of population-type characteristics of various kinds of stars, a statistical analysis of kinematical and distribution parameters of the same stars is performed at the Toruń Observatory. This has a twofold purpose: first, to provide a practical guide in selecting stars for observing programmes, second, to contribute to the understanding of relations existing between the physical and chemical properties of stars and their kinematics and distribution in the Galaxy.

A Stochastic Study on the Distribution Parameters of Random Individual Walking Excitations

AEI 2017 ◽  
2017 ◽  
Author(s):  
Zhiqiang Zhang ◽  
Bill Zhang ◽  
Jieqiang Wei ◽  
Peng Luo ◽  
Changhui Cui
Keyword(s):  
Distribution Parameters ◽  
Random Individual
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