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相关概念视频

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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...
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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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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...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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对于具有密度功率分歧的单元级模型,可靠的小面积估计.

Xijuan Niu1,2, Zhiqiang Pang1, Zhaoxu Wang1,2

  • 1Department of Statistics, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China.

PloS one
|November 16, 2023
PubMed
概括
此摘要是机器生成的。

本研究为单位级模型引入了一种可靠的估计方法,解决了小面积估计中异常值引起的问题. 与传统的实证贝叶斯式 (EB) 方法相比,新方法提高了准确性和平均平方误差 (MSE) 性能.

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科学领域:

  • 统计 统计 统计 统计
  • 计量经济学 计量经济学
  • 小面积估计 小面积估计

背景情况:

  • 单元级模型对于使用单元信息数据进行小面积估计至关重要.
  • 经验贝叶斯式 (EB) 估计是一种常见但异常值敏感的方法,导致膨胀的平均平方误差 (MSE).

研究的目的:

  • 为单位级模型提出一个可靠的估计方法,以减轻异常值的影响.
  • 为强大的估计器开发一个最佳的参数选择算法.

主要方法:

  • 引入了最小密度功率分歧函数,以进行可靠的参数估计.
  • 导出了强大的参数的非对称分布.
  • 采用了用于MSE估计小面积平均值的启动方法.

主要成果:

  • 拟议的强大方法在模拟和真实数据分析中表现出卓越的性能.
  • 成功解决异常情况,优于传统的EB方法.
  • 为单位和面积平均值提供准确的经验贝叶斯预测器.

结论:

  • 这种新的可靠估计方法有效地处理单位级模型中的异常值.
  • 在存在数据异常的情况下,为小面积估计提供了更好的准确性和可靠性.
  • 这种方法提高了单位级模型在统计分析中的实际适用性.