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A New Inverse Kumaraswamy Family of Distributions: Properties and Application 一个新的逆Kumaraswamy分布族:性质与应用
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-06-02 DOI: 10.18187/pjsor.v19i2.4295
A. M. Daghistani, Bander Al-Zahrani, M. Shahbaz
The Kumaraswamy distribution is an important probability distribution used to model several hydrological problems as well as various natural phenomena whose process values are bounded on both sides. In this paper, we introduce a new family of inverse Kumaraswamy distribution and then explore its statistical properties. Conventional maximum likelihood estimators are considered for the parameters of this distribution and estimation based on dual generalized order statistics is outlined. A particular sub-model of this family; namely, the inverse Kumaraswamy- Weibull distribution is considered and some of its statistical properties are obtained. Estimation efficiency is numerically evaluated via a simulation study and two real-data applications of the proposed distribution are provided as well.
Kumaraswamy分布是一种重要的概率分布,用于模拟几个水文问题以及过程值在两侧都有界的各种自然现象。在本文中,我们引入了一个新的逆Kumaraswamy分布族,并探讨了它的统计性质。对于这种分布的参数,考虑了传统的最大似然估计,并概述了基于对偶广义阶统计量的估计。该家族的特定子模型;即考虑了逆Kumaraswamy-Weibull分布,得到了它的一些统计性质。通过模拟研究对估计效率进行了数值评估,并提供了所提出分布的两个实际数据应用。
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引用次数: 0
GOLD DISTRIBUTION Another Look on the Generalization of Lindely Distribution GOLD分布——对Lindely分布推广的再认识
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-06-02 DOI: 10.18187/pjsor.v19i2.3255
Mohammad Al-Talib, Amjad D. Al-Nasser, E. Ciavolino
In this paper, a new generalization of one parameter Lindely distribution is proposed. The new distribution is a mixture distribution of Gamma distributions with fixed scale parameter and variable shape parameter. The distribution is called 'GOLD Distribution' as it is a generalization for several distributions such as exponential, Lindely, Sujatha, Amarendra, Devya and Shambhu distributions. The probability density and cumulative density functions are derived. Also, the statistical properties of the GOLD distribution are discussed. Parameter estimation using the maximum likelihood and the method of moments are given. Moreover, an illustration of the usefulness of the GOLD distribution in survival data analysis is discussed based on a real lifetime data.
本文提出了单参数Lindely分布的一个新的推广。新分布是具有固定标度参数和可变形状参数的伽玛分布的混合分布。这种分布被称为“GOLD分布”,因为它是指数分布、Lindely分布、Sujatha分布、Amarendra分布、Devya分布和Shambhu分布等几种分布的推广。导出了概率密度函数和累积密度函数。此外,还讨论了GOLD分布的统计性质。给出了利用最大似然法和矩量法进行参数估计的方法。此外,基于实际寿命数据,讨论了GOLD分布在生存数据分析中的有用性。
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引用次数: 1
The Ordinal Regression to Analyze Radical Intention of Muslim Indonesian Students through Personality Type and Tolerance Approach 从人格类型和容忍度分析印尼穆斯林学生激进倾向的有序回归
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-06-02 DOI: 10.18187/pjsor.v19i2.3932
A. Meiza
This paper presents a result of research about the student’s radical intention related to their tolerance and personality type. This research was a collaboration between Statistics, Psychology, and Politics. The main research variable that is radical intention has a psychological construct that is theoretically built with a social approach and political point of view. As a big country, Indonesia has various pluralism i.e ethnic and religion, so that it requires a high tolerance attitude to live in a harmony. Students as the next generation and important elements in society are expected to avoid intolerance. The research subjects were 175 students from an Islamic university in Indonesia. The data were analyzed with Ordinal Regression which intention of radicalism (ordinal) as the dependent variable, personality type (nominal), and tolerance attitude level (ordinal) as independent variables. The majority of these students have good moral values so they can tolerate the difference. From the regression analysis, the type personality and tolerance attitude have a significant effect on radicalism intention.
本文介绍了一个关于学生™激进倾向与他们的宽容度和性格类型有关。这项研究是统计学、心理学和政治学的合作。激进意图这一主要研究变量具有一种从社会方法和政治观点理论上构建的心理结构。印尼作为一个大国,有着多种多样的民族和宗教,因此需要高度宽容的态度才能和睦相处。学生作为下一代和社会中的重要分子,应该避免不容忍。研究对象是来自印度尼西亚一所伊斯兰大学的175名学生。以激进化意向(序数)为因变量,人格类型(标称)和容忍态度水平(序数)作为自变量,采用序数回归分析数据。这些学生中的大多数都有良好的道德价值观,因此他们能够容忍这种差异。回归分析表明,类型人格和宽容态度对激进主义倾向有显著影响。
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引用次数: 0
A Generalization of Burr Type XII Distribution with Properties, Copula and Modeling Symmetric and Skewed Real Data Sets Burr型XII分布的性质、Copula及对称和斜交实数据集建模的推广
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.3377
Mohamed G. Khalil, Emadeldin I. A. Ali
A new generalization of Burr type XII model is introduced and studied. The genesis of the new model is based on the family of Cordeiro et al. (2016). The new model generalizes at least eight important sub-models. The new density can be unimodal, symmetric and left skewed. Some useful properties related to the new model are derived. The Clayton Copula-based construction is used to generate many bivariate and multivariate type distributions. Graphically, we performed the simulation experiments to assess of the finite sample behavior of the estimations.
介绍并研究了Burr型XII模型的一个新的推广。新模型的起源是基于Cordeiro等人的家族。(2016)。新模型至少概括了八个重要的子模型。新的密度可以是单峰的、对称的和左偏的。导出了与新模型相关的一些有用特性。基于Clayton-Copula的构造用于生成许多二变量和多变量类型分布。从图形上看,我们进行了模拟实验,以评估估计的有限样本行为。
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引用次数: 1
Kumaraswamy Esscher Transformed Laplace Distribution: Properties, Application and Extensions Kumaraswamy Esscher变换拉普拉斯分布:性质、应用和扩展
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.3818
Dais George, Rimsha H.
In this article, we introduce a new generalized family of Esscher transformed Laplace distribution, namely the Kumaraswamy Esscher transformed Laplace distribution. We study the various properties of the distribution including the survival function, hazard rate function, cumulative hazard rate function and reverse hazard rate function. The parameters of the distribution are estimated using the maximum likelihood method of estimation. A real application of this distribution on breaking stress of carbon fibres is also considered. Further, we introduce and study the exponentiated and transmuted exponentiated Kumaraswamy Esscher transformed Laplace distributions.
本文引入了一种新的广义Esscher变换拉普拉斯分布族,即Kumaraswamy Esscher变换拉普拉斯分布。我们研究了该分布的各种性质,包括生存函数、风险率函数、累积风险率函数和反向风险率函数。利用估计的极大似然法对分布参数进行估计。本文还考虑了这种分布在碳纤维断裂应力上的实际应用。进一步,我们引入并研究了指数化和变指数化Kumaraswamy Esscher变换拉普拉斯分布。
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引用次数: 0
The Marshall-Olkin Pranav distribution: Theory and applications Marshall-Olkin-Pranav分布:理论与应用
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4058
Rehab Alsultan
The current paper presented new two-parameter life processes distribution, the Marshall-Olkin Pranav (MOEP) distribution. This study combines the Marshall-Olkin method with the Pranav distribution to produce a more accessible and flexible model used to perform data survival techniques. Some of its critical statistical features are presented in this study. For instance, we mentioned its survival , hazard, reversed hazard, and cumulative hazard rate function. Then we discussed its Moment generating functions, The characteristic function, Incomplete moments, R`enyi and Entropies, and stochastic orderings. The research utilized maximization of chance in estimating parameters. These tests are done through simulations to achieve the desired results. After its attainment, real-life data was used to test the new model, which possesses the best goodness of fit.
本文提出了一种新的双参数生命过程分布,即Marshall-Olkin-Pranav(MOEP)分布。这项研究将Marshall-Olkin方法与Pranav分布相结合,产生了一个更易于访问和灵活的模型,用于执行数据生存技术。本文介绍了它的一些关键统计特征。例如,我们提到了它的生存、危害、反向危害和累积危害率函数。然后讨论了它的矩生成函数、特征函数、不完全矩、R`enyi和熵以及随机序。该研究利用机会最大化来估计参数。这些测试是通过模拟来实现预期结果的。在实现后,使用真实数据来测试新模型,该模型具有最佳拟合优度。
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引用次数: 0
Comparison of Infinite Capacity FM/FEk/1 Queuing Performance Using Fuzzy Queuing Model and Intuitionistic Fuzzy Queuing Model with Erlang Service Rates 基于模糊排队模型和基于Erlang服务率的直觉模糊排队模型的无限容量FM/FEk/1排队性能比较
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4055
A. S, M. Shanmugasundari
In this work, we suggest an analytic technique with triangular fuzzy and triangular intuitionistic fuzzy numbers to compute the membership functions of considerable state-executing proportion in Erlang service models. The inter-entry rate, which is Poisson, and the admin (service) rate, which is Erlang, are both fuzzy-natured in this case, with FEk designating the Erlang probabilistic deviation with k exponentially phase. The numeric antecedents are shown to validate the model's plausibility, FM/FEk/1. A contextual inquiry is also carried out, comparing individual fuzzy figures. Intuitionistic fuzzy queueing models that are comprehensible are more categorical than fuzzy queueing models. Expanding the fuzzy queuing model to an intuitionistic fuzzy environment can boost the implementation of the queuing model. The purpose of this study is to assess the performance of a single server Erlang queuing model with infinite capacity using fuzzy queuing theory and intuitionistic fuzzy queuing theory. The fuzzy queuing theory model's performance evaluations are reported as a range of outcomes, but the intuitionistic fuzzy queuing theory model provides a myriad of values. In this context, the arrival and the service rate are both triangular and intuitionistic triangular fuzzy numbers. An assessment is made to find evaluation criteria using a design protocol in which fuzzy values are kept as they are and not made into crisp values, and two statistical problems are solved to understand the existence of the method.
在这项工作中,我们提出了一种三角模糊和三角直觉模糊数的分析技术来计算Erlang服务模型中相当大的状态执行比例的隶属函数。在这种情况下,进入间率(即泊松)和管理(服务)率(即Erlang)都是模糊性质的,其中FEk表示Erlang与k指数阶段的概率偏差。数值前因式FM/FEk/1验证了模型的合理性。上下文调查也进行,比较个别模糊数字。直觉模糊排队模型比模糊排队模型更具有可理解性。将模糊排队模型扩展到直观的模糊环境可以促进排队模型的实现。本研究的目的是利用模糊排队理论和直觉模糊排队理论来评估具有无限容量的单服务器Erlang排队模型的性能。模糊排队论模型的绩效评价报告为一系列结果,而直觉模糊排队论模型提供了无数的值。在这种情况下,到达率和服务率都是三角模糊数和直觉三角模糊数。采用一种保持模糊值不变而不使其变为清晰值的设计方案,对评价标准进行了评估,并解决了两个统计问题,以理解该方法的存在性。
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引用次数: 0
Prediction of KLCI Index Through Economic LASSO Regression Model and Model Averaging 利用经济LASSO回归模型和模型平均预测KLCI指数
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4214
Khuneswari Gopal Pillay, Soh Pei Lin
The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and the complexity in terms of identifying the factors that have a substantial impact on the Malaysian stock market has always been a contentious issue. In this study, the macroeconomic factors of exchange rate, interest rate, gold price, consumer price index, money supply M1, M2, and M3, industrial production, and oil price were discussed by using economic LASSO regression and Bayesian Model Averaging (BMA) with monthly average and monthly end time-series data spanning from January 2015 to June 2021, with a total of 78 observations by using the R Studio. The findings demonstrate that month-end data is better suited for stock market prediction than month-average data and that the BMA model is more suitable than the LASSO model, as seen by lower Mean Square Error of Prediction, MSE(P) and Residual Mean Square Error of Prediction, RMSE(P) values. The exchange rate, gold price, and money supply have a negative association with the dependent variables, while the consumer price index has a positive relationship associated with the dependent variables. The consumer price index is the most significant contributing factor, whereas gold price is the least significant. The result depicted that the KLCI index has no significant relationship with the variables interest rate, money supply M2, M1, industrial production index, and oil price. In conclusion, investors could specifically focus on the positive contributor and put lesser attention on improving their portfolio return.
金融时报证券交易所(FTSE)马来西亚Bursa KLCI指数是马来西亚经济增长发展的关键组成部分,在确定对马来西亚股票市场产生重大影响的因素方面的复杂性一直是一个有争议的问题。本研究采用经济LASSO回归和贝叶斯平均模型(BMA),利用2015年1月至2021年6月的月均和月末时间序列数据,利用R Studio共78个观测值,对汇率、利率、黄金价格、消费者价格指数、货币供应量M1、M2和M3、工业生产和油价等宏观经济因素进行了讨论。研究结果表明,月末数据比月平均数据更适合股市预测,BMA模型比LASSO模型更适合股市预测,预测均方误差、MSE(P)和预测残均方误差、RMSE(P)值都更小。汇率、金价、货币供应量与因变量呈负相关关系,而消费者物价指数与因变量呈正相关关系。消费者价格指数是最显著的影响因素,而黄金价格是最不显著的。结果表明,KLCI指数与利率、货币供应量M2、M1、工业生产指数、油价等变量关系不显著。综上所述,投资者可以特别关注积极的贡献者,而不太关注提高他们的投资组合回报。
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引用次数: 0
Multiclass Forecasting on Panel Data Using Autoregressive Multinomial Logit and C5.0 Decision Tree 基于自回归多项式Logit和C5.0决策树的面板数据多类预测
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4053
Muhlis Ardiansyah, Hari Wijayanto, Anang Kurnia, A. Djuraidah
Panel data is commonly used for the numerical response variables, while the literature for forecasting categorical variables on the panel data structure is still challenging to find. Forecasting is important because it is helpful for government policies. This study aimed to forecast multiclass or categorical variables on the panel data structure. The proposed forecasting models were autoregressive multinomial logit and autoregressive C5.0. The strategy applied so that the two models could be used for forecasting was to add autoregressive effects and fixed predictor variables such as location, time, strata, and month of observations. The autoregressive effect  was assumed to be a fixed effect and treated as a dummy variable. The data used was the category of land conditions through The Area Sampling Frame (ASF) survey conducted by the BPS-Statistics Indonesia. The evaluation of both models was based on classification and forecasting performance. Classification performance was obtained by dividing the dataset into 75% training data for modeling and 25% test data for validation and then repeated 200 times. The classification results showed that the autoregressive C5.0 accuracy was 86.48%, while the autoregressive multinomial logit was 83.97%. A comparison of forecasting performance was obtained by dividing the data into training and testing based on the time sequence. The result showed that the forecasting performance was worse than the classification performance. Autoregressive C5.0 had an accuracy of 77.43%, while autoregressive multinomial logit had 77.77%.
面板数据通常用于数值响应变量,而在面板数据结构上预测分类变量的文献仍然很难找到。预测很重要,因为它有助于政府政策。本研究旨在预测面板数据结构上的多类别或分类变量。所提出的预测模型为自回归多项式logit和自回归C5.0。使这两个模型可用于预测的策略是添加自回归效应和固定的预测变量,如位置、时间、地层和观测月份。自回归效应被假设为固定效应,并被视为伪变量。使用的数据是通过BPS印尼统计局进行的区域抽样框架(ASF)调查得出的土地状况类别。这两个模型的评估都是基于分类和预测性能。分类性能是通过将数据集划分为75%的训练数据用于建模和25%的测试数据用于验证来获得的,然后重复200次。分类结果表明,自回归C5.0的准确率为86.48%,而自回归多项式logit为83.97%。通过根据时间序列将数据划分为训练和测试,获得了预测性能的比较。结果表明,预测性能比分类性能差。自回归C5.0的准确率为77.43%,而自回归多项式logit的准确率则为77.77%。
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引用次数: 1
Asymptotic Normality of the Conditional Hazard Rate Function Estimator for Right Censored Data under Association 关联条件下右截断数据条件危险率函数估计的渐近正态性
IF 1.5 Q3 STATISTICS & PROBABILITY Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.3740
Samra Dhiabi, Ourida Sadki
In this paper, we study a smooth estimator of the conditional hazard rate function in the censorship model when the data exhibit some dependence structure. We show, under some regularity conditions, that the kernel estimator of the conditional hazard rate function suitably normalized is asymptotically normally distributed.
在本文中,我们研究了当数据表现出某种依赖结构时,审查模型中条件风险率函数的光滑估计。在一定的正则性条件下,我们证明了条件风险率函数适当归一化的核估计是渐近正态分布的。
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引用次数: 0
期刊
Pakistan Journal of Statistics and Operation Research
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