Estimation of three-parameter Fréchet distribution for the number of days from drug administration to remission in small sample sizes

T. Ogura, C. Shiraishi
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Abstract

In medical research, it is common to estimate parameters for each group and then evaluate the estimated parameters for each group without comparing the groups. However, researchers frequently want to determine whether the two distributions using the estimated parameters differ significantly between the two groups. For the Weibull distribution, the two-sample Kolmogorov-Smirnov test (two-sided) was used to examine whether the two distributions were significantly different between the two groups. Based on this, we developed a method to compare the two groups using a three-parameter Fréchet distribution. The number of days from drug administration to remission frequently followed a Fréchet distribution. It is appropriate to use a three-parameter Fréchet distribution with a location parameter because patients typically go into remission after several days of drug administration. We propose a minimum variance linear estimator with a hyperparameter (MVLE-H) method for estimating a three-parameter Fréchet distribution based on the MVLE-H method for estimating a three-parameter Weibull distribution. We verified the effectiveness of the MVLE-H method and the two-sample Kolmogorov-Smirnov test (two-sided) on the three-parameter Fréchet distribution using Monte Carlo simulations and numerical examples.
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小样本中从服药到病情缓解的天数的三参数弗雷谢特分布估计
在医学研究中,通常是为每组估计参数,然后评估每组的估计参数,而不对各组进行比较。然而,研究人员经常想确定使用估计参数的两个分布在两组之间是否有显著差异。对于 Weibull 分布,我们使用双样本 Kolmogorov-Smirnov 检验(双侧)来检验两组之间的两种分布是否存在显著差异。在此基础上,我们开发了一种使用三参数弗雷谢特分布对两组进行比较的方法。从用药到病情缓解的天数经常遵循弗雷谢特分布。使用带有位置参数的三参数弗雷谢特分布是合适的,因为患者通常会在服药数天后进入缓解期。我们在估计三参数韦布尔分布的 MVLE-H 方法的基础上,提出了带超参数的最小方差线性估计器(MVLE-H)方法,用于估计三参数弗雷谢特分布。我们利用蒙特卡罗模拟和数值示例验证了 MVLE-H 方法和双样本 Kolmogorov-Smirnov 检验(双侧)对三参数 Fréchet 分布的有效性。
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来源期刊
Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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Limitations of the propensity scores approach: A simulation study INAR(1) process with Poisson-transmuted record type exponential innovations Estimation of three-parameter Fréchet distribution for the number of days from drug administration to remission in small sample sizes Analysis of kidney infection data using correlated compound poisson frailty models Parametric analysis and model selection for economic evaluation of survival data
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