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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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Density00:56

Density

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Density is an important characteristic of substances, crucial in determining whether an object sinks or floats in a fluid. Its SI unit is kg/m3, and its cgs unit is g/cm3. The density of an object helps in identifying its composition, and also reveals information about the phase of the matter and its substructure. The densities of liquids and solids are roughly comparable, consistent with the fact that their atoms are in close contact. However, gases have much lower densities than liquids 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

Estimating Population Mean with Known Standard Deviation

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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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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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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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Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

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Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
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基于时刻的密度估计器的光滑级别选择.

Rosa M García-Fernández1, Federico Palacios-González1

  • 1Department of Quantitative Methods for Economics and Business, Faculty of Economics and Business Sciences, University of Granada, Granada, Spain.

Journal of applied statistics
|August 19, 2024
PubMed
概括

本研究提出了一种基于时刻的新方法,用于在多分辨率密度估计和内核密度估计中选择平滑参数. 与现有标准相比,这种方法在多式联运密度方面提供了更高的性能.

科学领域:

  • 统计 统计 统计 统计
  • 数据科学数据科学数据科学
  • 计算统计学 计算统计学

背景情况:

  • 密度估计对于理解数据分布至关重要.
  • 选择最佳的光滑参数 (带宽) 是非参数和多分辨率密度估计的一个关键挑战.
  • 像BIC和插件这样的现有方法有局限性,特别是在复杂的分发中.

研究的目的:

  • 引入一种用于选择带宽或平滑参数在多分辨率 (MR) 和非参数密度估计中的新方法.
  • 根据已确定的标准评估这种新方法的性能.

主要方法:

  • 拟议的方法分析了第二,第三和第四个中心时刻的演变以及不同带宽和分辨率水平的密度形状.
  • 适用于多分辨率密度估计 (MRDE) 和内核密度估计 (KDE).

主要成果:

  • 基于时刻的方法证明了多模式密度的性能提高.
  • 在多分辨率密度估计中优于贝叶斯信息标准 (BIC).
  • 在内核密度估计中优于插件方法.

结论:

  • 时刻方法为密度估计中的带宽选择提供了一个强大的方法.
关键词:
多分辨率密度估计的估计.带宽 带宽 带宽 带宽核密度估计核密度的估计.时刻和分辨率的分辨率水平.

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  • 对于多式联网分销来说,它特别有效.
  • 为现有的带宽选择技术提供了有价值的替代方案.