Relevant bibliographies by topics / Burr type XII distribution

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  2. Dissertations / Theses
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Academic literature on the topic 'Burr type XII distribution'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Burr type XII distribution"

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Al-Khazaleh, Ahmad M. H. "Transmuted Burr type XII distribution: a generalization of the Burr type XII distribution." International Mathematical Forum 11 (2016): 547–56. http://dx.doi.org/10.12988/imf.2016.6443.

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fawzy, hala, and hala fawzy. "Extended Burr Type XII Distribution." Egyptian Statistical Journal 66, no. 2 (2022): 17–41. http://dx.doi.org/10.21608/esju.2023.183619.1009.

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Ilikkan, Eda Selin, and Elif Yildirim. "The Type I Half Logistic-Burr XII lifetime distribution : Properties and application to survival data." Journal of Statistics and Management Systems 27, no. 5 (2024): 835–52. http://dx.doi.org/10.47974/jsms-934.

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Real life data cannot fit to the commonly known statistical model structures and therefore, different new models are needed in the modeling of this type of data. In this paper, we propose a new lifetime distribution called the Type I Half Logistic-Burr XII that can model different data from engineering and biomedicine science with its flexible structure. For this new distribution, we obtain the statistical properties including the hazard function, survival function, moment generating function, variance, quantile function, skewness and kurtosis. Moreover, the unknown parameters of the proposed type I half logistic-Burr XII distribution are obtained by using the maximum likelihood method. Application with two survival time data is presented to illustrate the effectiveness of the new distributions and it is shown to be better than the Burr XII, Topp-Leone Burr XII and Extended Weibull Log-Logistic distribution.
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Para, B. A., and T. R. Jan. "Discrete Generalized Burr-Type XII Distribution." Journal of Modern Applied Statistical Methods 13, no. 2 (2014): 244–58. http://dx.doi.org/10.22237/jmasm/1414815120.

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Khalil, Mohamed G., Khaoula Aidi, M. Masoom Ali, Nadeem S. Butt, Mohamed Ibrahim, and Haitham M. Yousof. "Modified Bagdonavicius-Nikulin Goodness-of-fit Test Statistic for the Compound Topp Leone Burr XII Model with Various Censored Applications." Statistics, Optimization & Information Computing 12, no. 4 (2024): 851–68. http://dx.doi.org/10.19139/soic-2310-5070-1447.

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The Poisson Topp Leone Burr XII distribution is extensively studied due to its broad relevance in analyzing censored real datasets from engineering, economics, and medicine. In this research, the distribution's versatility is highlighted through the analysis of four specific real datasets. The study compares the Poisson Topp Leone Burr XII distribution with nine extensions of the Burr type XII distribution to determine which offers the best fit for these datasets. To evaluate the goodness-of-fit of the Poisson Topp Leone Burr XII distribution under right censoring, a modified Bagdonavi\v{c}ius-Nikulin goodness-of-fit test statistic is introduced and applied. This new test statistic is utilized to validate the distributional fit for the Poisson Topp Leone Burr XII distribution across the four right-censored datasets. The modified Bagdonavi\v{c}ius-Nikulin test statistic is employed to assess distributional validation, specifically in the context of right censoring. The application of this statistic involves analyzing each of the four censored datasets to confirm the appropriateness of the Poisson Topp Leone Burr XII distribution for these scenarios. Additionally, to support the evaluation of the modified goodness-of-fit test statistic, the Barzilai-Borwein algorithm is utilized. This algorithm is employed within a simulation study to further assess the effectiveness and reliability of the modified Bagdonavi\v{c}ius-Nikulin test statistic, thereby ensuring robust validation of the Poisson Topp Leone Burr XII distribution against the observed real datasets.
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Saxena, Suneet. "Software Reliability Growth Modeling Involving Burr Type XII distribution and Fault Removal Efficiency." SRMS Journal of Mathmetical Science 5, no. 01 (2019): 11–13. http://dx.doi.org/10.29218/srmsmaths.v5i1.2.

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In software reliability analysis various authors have used Burr type XII distribution to model the failure pattern of the system due to its wide variety of flexible shapes. In particular cases it can be reduced to Exponential, Normal, Weibull, Log-logistic, Gamma distributions etc. In proposed paper software reliability growth model has been developed incorporating fault removal efficiency (FRE) and Burr type XII based testing effort function. FRE represents fraction of detected faults which are removed completely. Parameters of model are predicted by LSE whereas MSE is used to perform comparison analysis. Results validate better fitting of data set.
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Saripalli, Arun Kumar, Sridhar Akiri, Rekha Sarode, B. V. Nagarjuna Vasili, and M. Ramanaiah. "Comparative Performance of Burr Type XII 3P, Dagum Type I 3P and Log-Logistic 3P Distributions in Modeling Ozone (O₃), PM₁₀ and PM₂.₅ Concentrations." Research Journal of Chemistry and Environment 29, no. 4 (2025): 39–56. https://doi.org/10.25303/294rjce039056.

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This study investigates the suitability of three parameters continuous probability distributions-Burr Type XII 3P, Dagum Type I 3P and Log-Logistic 3P-in modeling secondary air pollutants: ozone (O₃), particulate matters (PM₁₀ and PM₂.₅) in Visakhapatnam, an urban region having rapid industrialization. By employing rigorous statistical techniques including maximum likelihood estimation (MLE) and bootstrapping, we estimate distribution parameters and validate model fit through diagnostic plots-skewness vs. kurtosis, P-P and Q-Q plots as well as goodness-of-fit test-statistics, such as Kolmogorov-Smirnov(KS), Anderson-Darling(AD) and Cramér von Mises(CvM) tests. Additional, performance metrics including Akaike information criterion(AIC), Bayesian information criterion(BIC), evaluation metrics like mean absolute error(MAE), mean absolute percentage error(MAPE), mean squared error(MSE), root mean squared error(RMSE) and coefficient of determination(R²) and cross-validation, were also applied to ensure model robustness. Results indicate that the Burr Type XII 3P distribution most effectively models the high variability and skewed nature of O₃ concentrations, while the Dagum Type I 3P distribution provides the best fit for PM₁₀ and both Burr Type XII 3P and Log-Logistic 3P distributions are suitable for PM₂.₅. These findings offer new insights into the behavior of secondary pollutants, supporting the development of robust air quality monitoring frameworks. R software facilitated all numerical analyses and visualizations of data suited to environmental data modeling.
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Ashraf, Mohammed Shareef. "Estimation of Mixed (Burr type XII and Exponential) Distribution Parameters." European Journal of Theoretical and Applied Sciences 2, no. 6 (2024): 747–54. https://doi.org/10.59324/ejtas.2024.2(6).66.

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Many mixed distributions with various number of parameters can be generated from current distributions using the blending parameters 𝑠𝑠𝑤𝑤; where 0≤𝑠𝑠𝜋𝜋≤1 and Σ𝑠𝑠𝜋𝜋𝐾𝐾𝐾 𝑤𝑤=1=1. This research goals to utilize the concept of combining two distributions (Burr type XII and Exponential) with unbalanced scale coefficients to obtain a mixed distribution. These generated distributions were combined using the blending parameter 𝑤𝑤𝑘𝑘 which determines the percentage of contribution rate of each distribution to the resulting distribution. The significance of this study lies in developing a more adaptable distribution that can be used in statistical applications. Two estimation methods, namely maximum likelihood estimation (MLE) and ordinary least squares (OLS), were used to estimate the parameters of the generating mixed distribution. Simulation studies were also conducted to verify the properties of the generating mixed distribution and utilize the two estimating methods to estimate its parameters.
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Sánchez, Ewin. "Burr type-XII as a superstatistical stationary distribution." Physica A: Statistical Mechanics and its Applications 516 (February 2019): 443–46. http://dx.doi.org/10.1016/j.physa.2018.10.044.

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Al-Hussaini, Essam K. "A characterization of the Burr type XII distribution." Applied Mathematics Letters 4, no. 1 (1991): 59–61. http://dx.doi.org/10.1016/0893-9659(91)90123-d.

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Dissertations / Theses on the topic "Burr type XII distribution"

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Pant, Mohan Dev. "Simulating Univariate and Multivariate Burr Type III and Type XII Distributions Through the Method of L-Moments." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/401.

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The Burr families (Type III and Type XII) of distributions are traditionally used in the context of statistical modeling and for simulating non-normal distributions with moment-based parameters (e.g., Skew and Kurtosis). In educational and psychological studies, the Burr families of distributions can be used to simulate extremely asymmetrical and heavy-tailed non-normal distributions. Conventional moment-based estimators (i.e., the mean, variance, skew, and kurtosis) are traditionally used to characterize the distribution of a random variable or in the context of fitting data. However, conventional moment-based estimators can (a) be substantially biased, (b) have high variance, or (c) be influenced by outliers. In view of these concerns, a characterization of the Burr Type III and Type XII distributions through the method of L-moments is introduced. Specifically, systems of equations are derived for determining the shape parameters associated with user specified L-moment ratios (e.g., L-Skew and L-Kurtosis). A procedure is also developed for the purpose of generating non-normal Burr Type III and Type XII distributions with arbitrary L-correlation matrices. Numerical examples are provided to demonstrate that L-moment based Burr distributions are superior to their conventional moment based counterparts in the context of estimation, distribution fitting, and robustness to outliers. Monte Carlo simulation results are provided to demonstrate that L-moment-based estimators are nearly unbiased, have relatively small variance, and are robust in the presence of outliers for any sample size. Simulation results are also provided to show that the methodology used for generating correlated non-normal Burr Type III and Type XII distributions is valid and efficient. Specifically, Monte Carlo simulation results are provided to show that the empirical values of L-correlations among simulated Burr Type III (and Type XII) distributions are in close agreement with the specified L-correlation matrices.
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Johnson, Richard. "Accelerated life testing and the Burr XII distribution." Thesis, Swansea University, 2003. https://cronfa.swan.ac.uk/Record/cronfa42233.

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This thesis looks at extending previous work in the field of accelerated life testing experiments. Hitherto, much investigation in this field has centred on a few standard statistical lifetime distributions, the Weibull being particularly popular. We consider a more flexible distribution, the Burr XII, and compare theoretical and simulated results; we also examine a number of examples. This comparison is interesting particularly since there is a limiting relationship between the two distributions, a property that is exploited in this thesis. Having laid down the necessary groundwork, we then proceed to fit the Weibull and Burr XH models to completely failed, published data sets and compare results. In order to assess our ability to make small sample theoretical inspections, we then establish the expected Fisher information matrices for the accelerated and non-accelerated Burr XH models, validating our results through simulations. The limiting link between the two distributions is then investigated, where we see that we can determine whether the Burr XH distribution will provide a better fit to a given data set than the Weibull, by fitting the Weibull distribution and then calculating a simple discriminating function. Type I censoring for the accelerated models is then considered. As for complete data, we formulate the expected Fisher information matrices. We then examine theoretical agreement between these results and those obtained for completely failed data. We also examine the agreement between Type I simulated and theoretical results. Finally, we investigate the practical applications of our work, and consider in particular extrapolations to lower operating stresses and the expected lifetimes of items tested at those stresses. Our investigations, although based on limited parameter values, illustrate useful conclusions on the conduct of such experiments and, consequently, are of potential value to a practitioner who, prior to carrying out an experiment, would like to know what combination of stresses and sample sizes would return the most information about the running time of items at the normal operating stress. After summarising our results and conclusions, some ideas for future research are detailed.
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Lanjoni, Beatriz Rezende. "O modelo Burr XII geométrico: propriedades e aplicações." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-17122013-085812/.

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No presente trabalho são propostos dois modelos para dados censurados baseados na mistura da distribuição geométrica e na distribuição Burr XII considerando duas ativações latentes, máximo e mínimo. A distribuição Burr XII tem três parâmetros e é uma generalização da distribuição log-logística. Por sua vez a distribuição Burr XII Geométrica tipo I e tipo II tem quatro parâmetros e são generalizações da distribuição Burr XII relacionados as ativações latentes do mínimo e máximo respectivamente. Foram apresentadas algumas propriedades das duas novas distribuições tais como momentos, assimetria, curtose, função geradora de momentos e desvio médio. Além disso, foi intriduzido os modelos de regressão correspondentes, log Burr XII Geométrica tipo I e log Burr XII Geométrica tipo II. Adicionalmente foi desenvolvido um modelo de sobrevivência com fração de cura assumindo que o número de causas competitivas do evento de interesse segue a distribuição geométrica e o tempo do evento segue a distribuição Burr XII. Para todos os modelos desenvolvidos foi utilizado o método da máxima verossimilhança para estimar os parâmetros, que possibilita a construção de intervalos de confiança e testes de hipóteses. Por fim, são apresentadas três aplicações para ilustrar os modelos propostos.<br>In this paper are proposed two models for censored data based on the mixture of geometric distribution and Burr XII distribution considering two latent activations, maximum and minimum. The Burr XII distribution has three parameters and is a generalization of the log-logistic distribution. On the other hand Burr XII Geometric type I distribution and type II has four parameters and are a generalization of the Burr XII distribution related to minimum and maximum activations respectively. It were presented some properties of the news distributions such as moments, skewness, kurtosis, moment generating function and mean deviation. Furthermore, it was introduced two regression models, the log Burr XII Geometric type I and the log Burr XII Geometric type II. Additionally a new cure rate survival was formulated by assuming that the number of competing causes of the event of interest has the geometric distribution and the time to this event follows Burr XII distribution. For all models was developed the maximum likelihood method to estimate the parameters, which allows the construction of confidence intervals and hypothesis testing. Finally, three applications are presented to illustrate the proposed models.
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Kim, Woosuk. "Statistical Inference on Dual Generalized Order Statistics for Burr Type III Distribution." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396533232.

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Brito, Edleide de. "Algumas novas distribuições: desenvolvimento e aplicações." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-12082014-150647/.

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Nos últimos anos, diversos autores têm concentrado seus esforços na generalização de distribuições de probabilidades obtendo, dessa forma, maior flexibilidade e, consequentemente, ganho na análise de dados e na capacidade de incorporar um grande número de sub-modelos nas distribuições generalizadas. Neste trabalho, serão apresentadas duas novas distribuições de probabilidade: McGumbel e gama Burr XII; e uma nova família de distribuições de probabilidade: Marshall-Olkin binomial negativa. Algumas propriedades das novas distribuições são apresentadas e o método de máxima verossimilhança foi utilizado para estimar os parâmetros dos modelos propostos.<br>In recent years, several authors have concentrated their efforts on the generalization of probability distributions obtained in this way more flexibility and hence gain in data analysis and the ability to incorporate a large number of sub-models in the generalized distributions. In this work, two new probability distributions will be presented: MacDonald Gumbel and gamma Burr XII; and a new family of probability distributions: negative binomial Marshall-Olkin. Some properties of the new distributions are presented and the method of maximum likelihood was used to estimate the parameters of the proposed models.
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Ahmad, Shafiq, and Shafiq ahmad@rmit edu au. "Process capability assessment for univariate and multivariate non-normal correlated quality characteristics." RMIT University. Mathematical and Geospatial Sciences, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091127.121556.

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In today's competitive business and industrial environment, it is becoming more crucial than ever to assess precisely process losses due to non-compliance to customer specifications. To assess these losses, industry is extensively using Process Capability Indices for performance evaluation of their processes. Determination of the performance capability of a stable process using the standard process capability indices such as and requires that the underlying quality characteristics data follow a normal distribution. However it is an undisputed fact that real processes very often produce non-normal quality characteristics data and also these quality characteristics are very often correlated with each other. For such non-normal and correlated multivariate quality characteristics, application of standard capability measures using conventional methods can lead to erroneous results. The research undertaken in this PhD thesis presents several capability assessment methods to estimate more precisely and accurately process performances based on univariate as well as multivariate quality characteristics. The proposed capability assessment methods also take into account the correlation, variance and covariance as well as non-normality issues of the quality characteristics data. A comprehensive review of the existing univariate and multivariate PCI estimations have been provided. We have proposed fitting Burr XII distributions to continuous positively skewed data. The proportion of nonconformance (PNC) for process measurements is then obtained by using Burr XII distribution, rather than through the traditional practice of fitting different distributions to real data. Maximum likelihood method is deployed to improve the accuracy of PCI based on Burr XII distribution. Different numerical methods such as Evolutionary and Simulated Annealing algorithms are deployed to estimate parameters of the fitted Burr XII distribution. We have also introduced new transformation method called Best Root Transformation approach to transform non-normal data to normal data and then apply the traditional PCI method to estimate the proportion of non-conforming data. Another approach which has been introduced in this thesis is to deploy Burr XII cumulative density function for PCI estimation using Cumulative Density Function technique. The proposed approach is in contrast to the approach adopted in the research literature i.e. use of best-fitting density function from known distributions to non-normal data for PCI estimation. The proposed CDF technique has also been extended to estimate process capability for bivariate non-normal quality characteristics data. A new multivariate capability index based on the Generalized Covariance Distance (GCD) is proposed. This novel approach reduces the dimension of multivariate data by transforming correlated variables into univariate ones through a metric function. This approach evaluates process capability for correlated non-normal multivariate quality characteristics. Unlike the Geometric Distance approach, GCD approach takes into account the scaling effect of the variance-covariance matrix and produces a Covariance Distance variable that is based on the Mahanalobis distance. Another novelty introduced in this research is to approximate the distribution of these distances by a Burr XII distribution and then estimate its parameters using numerical search algorithm. It is demonstrates that the proportion of nonconformance (PNC) using proposed method is very close to the actual PNC value.
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Chen, Yung-Lin, and 陳詠霖. "Statistical analysis of two-parameter Burr-XII distribution under Type II progressive censoring with random removals." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/45346315468621242679.

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碩士<br>淡江大學<br>統計學系<br>92<br>In many application of industrial experiment, life-tests are often one of the main research topics. Censoring arises under the consideration of saving time and cost. There are several types of censoring schemes and the Progressively Type II censoring scheme is one of those. When the failure factor of the product is fatigue or aging, the two-parameter Burr-XII distribution with unimodal failure rate function will be more appropriate. Therefore, the interval estimations of two parameters and the hypothesis testing of shape parameter for Burr-XII distribution under Type II progressive censoring with random removals including Binomial random removals and uniform removals are proposed in this research. There are (m-1) pivotal quantities are proposed, where m is the number of observations for Type II progressive censoring sample. Monte Carlo simulation is used to assess the behavior of these pivotal quantities based on the length of confidence interval or the area of confidence region or the power for various combination of (n,m,p). Some numerical examples are also given to demonstrate the proposed methods.
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Liu, Chia-Cheng, and 劉家成. "Step Partially Accelerated Life Tests for Burr XII Distribution with Type I Censored Data:MLE Performance in Small Sample." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/46961606579767425415.

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碩士<br>淡江大學<br>統計學系碩士班<br>100<br>The lifetime of products under normal conditions endures a long period of time because of high reliability of products. Reducing the sample size is the only way to solve this problem. It is difficult to obtain information about the lifetime of products under normal conditions. In order to assure rapid failure and then shorten the testing period, accelerated life test (ALT) is commonly used to obtain information about the lifetime of products quickly. In some cases, the failure model relating the lifetime of products and the stress are not known or cannot be assumed, especially for the new products. So step-stress partially accelerated life tests are the optional choice. This article considers step-stress partially accelerated life tests for small samples under Burr XII distribution. We use maximum likelihood method to estimate the distribution parameters and acceleration factor. The estimates are obtained by solving two nonlinear equations simultaneously. In addition, we also perform simulation of the asymptotic variance and confidence intervals of the parameters to evaluate the empirical performances of our estimators.
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Chen, Tzu-Chin, and 陳姿瑾. "A testing procedure for the lifetime performance index of products with Burr XII distribution under progressive type I interval censoring." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/s3emd8.

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碩士<br>淡江大學<br>統計學系碩士班<br>102<br>In recent years, consumers are in the pursuit of more stringent product quality requirements for many high-tech products such as tablet, smart mobile phones, etc. In practice, many researchers have developed a variety of methods to assess the quality of the product and the method of process capability indices (PCIs) is one of them. This research is focusing on the lifetime of products following the Burr XII distribution. The maximum likelihood estimator is used to estimate the lifetime performance index (CL) based on the progressive type I interval censored sample. The asymptotic distribution of this estimator is also investigated. We use this estimator and two kinds of bootstrap to develop three kinds of new hypothesis testing algorithmic procedure in the condition of known lower specification limit L. Finally, two practical examples are given to illustrate the use of this testing algorithmic procedure to determine whether the process is capable.
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Lin, Ya-Li, and 林雅莉. "Statistical inference about the shape parameter of the Burr type XII and Lognormal distributions based on multiply type II censored sample." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/01416020342338538728.

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碩士<br>淡江大學<br>統計學系碩士班<br>93<br>In human life, we often encounter the situation of getting censored sample, for example, because of the restriction of time and cost or human mistake, we can’t get all observations. In practice, there are often these issues concerned in the data of the reliability or survival analysis. In face of this censored data, we can’t apply traditional statistical inference to perform our analysis on it. Therefore, what we discuss in this paper is statistical inference with multiply type II censored sample. In this paper, we discuss the lifetime distribution with the unimodal shape or reversed bathtub shape failure rate function under the multiply type II censored sample. First, we provide 18 pivotal quantities to test the shape parameter of the two lifetime distributions and establish confidence interval of the shape parameter under the multiply type II censored sample. Secondly, we also find the best test statistic based on their most power of test among all test statistics. In addition, we obtain the best pivotal quantities with the shortest tolerance length. Finally, we give two examples and the Monte Carlo simulation to assess the behavior (including higher power and more shorter length of confidence interval) of these pivotal quantities for testing null hypotheses under given significance level and establishing confidence interval of the shape parameter under the given confidence coefficient.
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Books on the topic "Burr type XII distribution"

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Cheng, Russell. Embedded Distributions: Two Numerical Examples. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0007.

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This chapter illustrates use of (i) the score statistic and (ii) a goodness-of-fit statistic to test if an embedded model provides an adequate fit, in the latter case with critical values calculated by bootstrapping. Also illustrated is (iii) calculation of parameter confidence intervals and CDF confidence bands using both asymptotic theory and bootstrapping, and (iv) use of profile log-likelihood plots to display the form of the maximized log-likelihood and scatterplots for checking convergence to normality of estimated parameter distributions. Two different data sets are analysed. In the first, the generalized extreme value (GEVMin) distribution and its embedded model the simple extreme value (EVMin) are fitted to Kevlar-fibre breaking strength data. In the second sample, the four-parameter Burr XII distribution, its three-parameter embedded models, the GEVMin, Type II generalized logistic and Pareto and two-parameter embedded models, the EVMin and shifted exponential, are fitted to carbon-fibre strength data and compared.
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Cheng, Russell. Examples of Embedded Distributions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0006.

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This chapter gives examples of probability distributions that, in their conventional parametrization, contain embedded models. Embeddedness is not intrinsic but depends on the parametrization. The simplest way to reveal and remove embeddedness is to reparametrize and make the log-likelihood, L, expandable as a Maclaurin series of one parameter, α‎: L = L0 + L1α‎ + L2α‎2 + … with L0 the log-likelihood of the embedded model hidden in the original parametrization. The quantity L1, rescaled using the information matrix, is the score statistic which can be used for formally comparing the original and embedded model fits. Embeddedness occurs in many distributions if there is a shifted threshold parameter. Examples given in the chapter are the Burr XII, gamma, generalized extreme value, inverse Gaussian, inverted gamma, logistic, loglogistic, lognormal, loggamma, Pareto, and Weibull distributions. Another interesting example occurs in early parametrizations of the stable law distribution.
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Book chapters on the topic "Burr type XII distribution"

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AL-Hussaini, Essam K., and Mohammad Ahsanullah. "Family of Exponentiated Burr Type XII Distributions." In Atlantis Studies in Probability and Statistics. Atlantis Press, 2015. http://dx.doi.org/10.2991/978-94-6239-079-9_5.

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Polosin, Vitaly G. "Study of Quantile Shape Measures of the Burr Type XII." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-95649-2_12.

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Coelho-Barros, Emílio Augusto, Jorge Alberto Achcar, and Josmar Mazucheli. "Mixture and Non-mixture Cure Rate Model Considering the Burr XII Distribution." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13881-7_24.

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Cetinkaya, Volkan, and Nazlı Gülfem Gidener. "Analysis of Turkey." In Advances in Logistics, Operations, and Management Science. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8474-6.ch005.

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This chapter aims to obtain the optimal routings for intermodal transport corridor from Turkey to the United Kingdom through a model based on the principles of 0-1 goal programming method, optimizing conflicting objectives such as minimizing transport costs, transit-time, and maximizing transit-time reliability. Cost and transit-time data in the study were obtained from a shipper company and used such an input for 0-1 goal programming model. The secondary aim in this study is to measure transit-time reliability with the transit-time data regarding conducted shipments. Thus, the Burr distribution (type XII) has been used to represent the observed transit-time data to measure the transit-time variability. The proposed model shows that transit-time reliability can play a more decisive role than transport cost and transit-time factor for competitiveness of intermodal transport routes.
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Conference papers on the topic "Burr type XII distribution"

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Tsai, Tzong-Ru, Jyun-You Jiang, Yuhlong Lio, Nan Jiang, and Ya-Yen Fan. "Parameter Estimation of the Burr Type XII Distribution with a Progressively Interval-Censored Scheme Using Genetic Algorithm." In International Conference on Industrial Application Engineering 2015. The Institute of Industrial Applications Engineers, 2015. http://dx.doi.org/10.12792/iciae2015.036.

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Sabr, Murtadha Rahman, and Alaa Khlaif Jiheel. "Pre-test shrinkage estimation on Burr XII distribution using progressive type II censored sample under precautionary loss function." In 6TH INTERNATIONAL CONFERENCE FOR PHYSICS AND ADVANCE COMPUTATION SCIENCES: ICPAS2024. AIP Publishing, 2025. https://doi.org/10.1063/5.0265611.

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Ko, Han-Ok, Jae-Boong Choi, and Young-Jin Kim. "A Robust Method to Determine Statistical Parameters for Cleavage Fracture Evaluation." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77214.

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Abstract:
During the last couple of decades, several design guidelines and codes relating to major nuclear components have been introduced and revised for risk-based design and evaluation. It is an important process to determine statistical parameters in practice, for instance, by using the traditional method of moments and Maximum Likelihood Method (MLM). Since appropriate estimation of the parameters is not easy due to mathematical complexity, a robust method adopting concepts of both Chi-Square test and genetic algorithm (GA) is proposed in this paper. The Chi-Square test is a useful technique to get the goodness-of-fit of distributions, which is represented in terms of error between observed frequencies and frequencies calculated by assumed probability density function (PDF) of certain statistical distribution. The GA is an efficient optimization algorithm to solve nonlinear optimum problems. Using the Chi-Square test, statistical parameters can be determined and transferred to an optimum problem, and then solved by the GA employing proper nonlinear objective function. Reliability of the proposed method is verified against fracture toughness test data sets of SA508 reactor pressure vessel material obtained from PCVN specimens at various temperatures. The large scatter of experimental data is examined in use of a distribution reported by Neville and Kennedy, Burr type III and XII distributions by Nadarajah and Kortz as well as well-known Weibull distribution. A systematic assessment is carried out by using the new method and its results are compared with corresponding ones derived from the traditional method. Pros and corns of the alternative distributions as well as technical findings from the statistical assessment are fully discussed to show applicability of them.
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Abbasi, B., S. Z. Hosseinifard, and M. Abdollahian. "On the Estimating Burr XII Distribution Parameters." In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.165.

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Mahdi, Montadher J., and Mushtaq K. Abd Al-Rahem. "New cubic transmuted Burr XII distribution, properties and application." In THE SECOND INTERNATIONAL SCIENTIFIC CONFERENCE (SISC2021): College of Science, Al-Nahrain University. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0119302.

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Khalaf, Alaa Abdulrahman, and Mundher Abdullah Khaleel. "The Odd Burr XII Exponential distribution: Properties and applications." In 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS, AI, INFORMATION AND COMMUNICATION TECHNOLOGIES: ICMAICT2023. AIP Publishing, 2025. https://doi.org/10.1063/5.0258451.

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Wei, Jinfen, Baowei Song, Weian Yan, and Zhaoyong Mao. "Reliability estimations of Burr-XII distribution under entropy loss function." In 2011 9th International Conference on Reliability, Maintainability and Safety (ICRMS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icrms.2011.5979276.

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Sahmey, Zeena Kamel, and Qasim Nasir Husain. "The Odd Burr XII inverse Weibull distribution: Properties and application." In 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS, AI, INFORMATION AND COMMUNICATION TECHNOLOGIES: ICMAICT2023. AIP Publishing, 2025. https://doi.org/10.1063/5.0258873.

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Rani, Seema, and N. Ahmad. "Software Reliability Growth Modeling with Burr Type XII using Fuzzy Logic." In 2020 5th International Conference on Computing, Communication and Security (ICCCS). IEEE, 2020. http://dx.doi.org/10.1109/icccs49678.2020.9277460.

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Zhou, Jie, and Xingshi He. "Bayes statistical analysis of Burr XII distribution in random censoring life test." In 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2013. http://dx.doi.org/10.1109/qr2mse.2013.6625780.

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