在多变量零截面测量错误模型中,符合正常曲率和检测蒙蔽观测
Reiko Aoki1, Juan P Mamani Bustamante1, Cibele M Russo1
1Instituto de Ciências Matemáticas e de Computaç ao, Universidade de São Paulo, São Carlos, Brazil.
Journal of applied statistics
|June 12, 2024
概括
这项研究引入了一种新的方法,即向前搜索的符合正常曲率,以改善测量错误模型中的影响诊断. 这种方法可以更好地检测隐藏的有影响力的观测,这对于可靠的统计分析至关重要.
科学领域:
- 统计 统计 统计 统计
- 数据分析 数据分析
- 制药科学 制药科学
背景情况:
- 在实际数据分析中,测量错误很常见.
- 在模型装配后,影响诊断是必不可少的.
- 对于测量误差模型的局部影响方法可以错过有影响力的观测.
研究的目的:
- 提出一种新的方法,用于检测测量误差模型中的蒙面影响性观测.
- 在存在测量错误时提高统计诊断的可靠性.
- 为了应对制药过程分析的挑战,特别是液晶体固体剂量稳定性.
主要方法:
- 使用符合正常曲率与前进搜索算法相结合.
- 开发易于解释的图表,以可视化诊断结果.
- 将方法应用于真实和模拟数据集,包括制药过程数据.
主要成果:
- 拟议的方法有效地识别了传统的局部影响技术错过的有影响力的观察结果.
- 可视化帮助理解不同扰动方案的影响.
- 在各种数据集上证明适用性,包括关键药品质量控制场景.
结论:
- 与前进搜索相一致的正常曲率为测量错误模型中的影响诊断提供了一个强大的替代方案.
- 这种技术对于确保数据质量和模型可靠性是有价值的,特别是在诸如制药制造等敏感应用中.
- 改进的诊断有助于更好地理解和控制可能存在测量错误的过程.
相关概念视频
Calibration Curves: Linear Least Squares
1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
476
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
476
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
123
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,...
123
Truncation in Survival Analysis
194
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
194
One-Way ANOVA: Equal Sample Sizes
3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K
Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K


