Estimate Stress‐Strength Reliability Model Using Rayleigh and Half‐Normal Distribution

Alamri, Osama Abdulaziz; El‐Raouf, M. M. Abd; Ismail, Eman Ahmed; Almaspoor, Zahra; Alsaedi, Basim S. O.; Khosa, Saima K.; Yusuf, M.

doi:10.1155/2021/7653581

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…As can be seen from the above, the skewness and kurtosis of all the differences are not 0, and it can be seen for the first time that all the differences do not obey the normal distribution [25][26][27][28][29][30]. Among them, it can be seen that the yield difference of the Shanghai Composite Index is large, and its kurtosis is also large, indicating that the exchange rate is heavy and the exchange rate is significant close to the means [31][32][33][34][35].…”

Section: Variable Selection Andmentioning

confidence: 91%

Quantitative Evaluation Model of Stock Market Liquidity by Macroeconomic Factors

Wang

2022

Mathematical Problems in Engineering

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In order to further understand the effects of macroeconomic factors on the stock market volatility and liquidity and solve the problem that the traditional volatility measurement model loses high-frequency data information in the modeling of the influence of macroeconomic factors on stock market volatility, monthly consumer price index, daily exchange rate, and monthly money supply are taken as the main indicators to investigate the stock market liquidity in the research. Meanwhile, CARCH-MIDAS model is used to investigate the factors affecting stock market liquidity. Through the model test, it is found that the H value of the volatility effect model of the three factors is 0.0307, and the H value of the horizontal effect model is 0.0220, and the result of the horizontal effect model is closest to 1%. The results show that CARCH-MIDAS model is relatively accurate in quantitative evaluation and prediction of the stock market liquidity and volatility.

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Section: Variable Selection Andmentioning

confidence: 91%

Quantitative Evaluation Model of Stock Market Liquidity by Macroeconomic Factors

Wang

2022

Mathematical Problems in Engineering

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“…where 𝑚 𝑟,𝑘+𝑢−1 is obtained from Equation (10). Various measures can be obtained from the raw moments of 𝑋 𝑘∶𝑛 , including the skewness and kurtosis coefficients, L-moments, enabling the establishment of L-scale, L-skewness, and L-kurtosis, among additional measures.…”

Section: Order Statisticsmentioning

confidence: 99%

Exponentiated half‐logistic Weibull distribution with reliability inference

El‐Awady,

El‐Monsef,

Elbaz

2024

Quality & Reliability Eng

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This paper presents an in‐depth analysis of the exponentiated half‐logistic Weibull (EHLW) distribution, investigating its fundamental statistical properties and exploring parameters estimation using both maximum likelihood and Bayesian approaches. Specifically, our focus centers on the analysis of stress–strength reliability, denoted as , where the strength variable X follows EHLW distribution, and the stress variable Y follows either Weibull distribution or the EHLW distribution. The estimation of the parameter R is discussed in both scenarios, employing both Maximum Likelihood and Bayesian approaches. Furthermore, we calculate that the asymptotic confidence interval, percentile bootstrap interval, and the highest probability density credible interval are obtained for the stress–strength parameter R. In order to assess the efficiency of these estimation techniques, simulation studies are conducted, providing valuable insights into the performance of each estimation approach. Finally, the proposed reliability model is applied to real datasets, highlighting its practical significance.

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“…We obtain the Rényi entropy of the HMF distribution by rearranging and increasing the power of the PDF of the HMF to λ and following the same procedure used to obtain the rth moments. e extent to which the strength of a system can withstand the stress it is subjected to is measured using the stress-strength reliability [24].…”

Section: Propositionmentioning

confidence: 99%

Harmonic Mixture Fréchet Distribution: Properties and Applications to Lifetime Data

Ocloo

Brew

Nasiru³

et al. 2022

International Journal of Mathematics and Mathematical Sciences

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In this study, we propose a four-parameter probability distribution called the harmonic mixture Fréchet. Some useful expansions and statistical properties such as moments, incomplete moments, quantile functions, entropy, mean deviation, median deviation, mean residual life, moment-generating function, and stress-strength reliability are presented. Estimators for the parameters of the harmonic mixture Fréchet distribution are derived using the estimation techniques such as the maximum-likelihood estimation, the ordinary least-squares estimation, the weighted least-squares estimation, the Cramér–von Mises estimation, and the Anderson–Darling estimation. A simulation study was conducted to assess the biases and mean square errors of the estimators. The new distribution was applied to three-lifetime datasets and compared with the classical Fréchet distribution and eight (8) other extensions of the Fréchet distribution.

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Estimate Stress‐Strength Reliability Model Using Rayleigh and Half‐Normal Distribution

Abstract: In the field of life testing, it is very important to study the reliability of any component under testing. One of the most important subjects is the “stress-strength reliability” term which always refers to the quantity P X > Y … Show more

Cited by 8 publications

References 19 publications

Quantitative Evaluation Model of Stock Market Liquidity by Macroeconomic Factors

Quantitative Evaluation Model of Stock Market Liquidity by Macroeconomic Factors

Exponentiated half‐logistic Weibull distribution with reliability inference

Harmonic Mixture Fréchet Distribution: Properties and Applications to Lifetime Data

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