一个新的两个参数混合家族的一般分布:统计学属性和应用
Alaa R El-Alosey1, Mohammed S Alotaibi1, Ahmed M Gemeay1
1Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt.
Heliyon
|December 17, 2024
概括
研究人员开发了一种新的玛指数指数分布 (MGExED) 混合物. 这种灵活的统计模型在现实数据分析中表现出与现有分布相比更高的性能.
科学领域:
- 统计 统计 统计 统计
- 概率理论的概率理论是什么
- 数学建模的数学建模
背景情况:
- 开发灵活的概率分布对于准确的统计建模至关重要.
- 现有的分布家族可能无法充分捕捉所有现实数据的复杂性.
- 需要新的发行版,具有增强的装配能力.
研究的目的:
- 通过混合方法引入一个新的两参数G分布家族.
- 调查玛指数指数分布 (MGExED) 混合物的统计性质.
- 通过使用真实数据集来评估MGExED的实际适用性和优势.
主要方法:
- 使用混合方法推导一种新的分布的G-家族.
- 统计性质的分析确定:MGExED的量子函数,时刻和顺序统计.
- 使用各种方法进行参数估计,通过蒙特卡洛模拟评估准确度.
- 将MGExED模型应用于两个不同的现实世界数据集.
主要成果:
- 提出的G-家族及其子模型MGExED成功衍生出来.
- 确定了MGExED的主要统计属性.
- 蒙特卡洛模拟证实了参数估计方法的准确性.
- 与基于真实数据的既定分布相比,MGExED显示出更高的灵活性和适合性.
结论:
- 新的MGExED为统计建模提供了一个灵活而有效的替代方案.
- 该分布在真实数据集上的表现突显了其实际实用性.
- MGExED具有在统计分析和建模中广泛应用的潜力.
相关概念视频
Distributions to Estimate Population Parameter
4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
Probability Distributions
6.8K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Parametric Survival Analysis: Weibull and Exponential Methods
356
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
356
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
1.6K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
1.6K
Binomial Probability Distribution
10.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
10.2K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
113
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
113


