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相关概念视频

Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.0K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.0K
What are Estimates?01:06

What are Estimates?

5.4K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
5.4K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
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...
8.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
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 +...
8.9K
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

712
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...
712

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相关实验视频

Updated: Sep 12, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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贝叶斯分层回归模型的近似交叉验证平均估计值.

Amy Zhang1, Michael J Daniels2, Changcheng Li3

  • 1Department of Statistics, The Pennsylvania State University, University Park, PA.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|August 7, 2025
PubMed
概括
此摘要是机器生成的。

我们介绍了一种在贝叶斯等级回归模型 (BHRMs) 中进行交叉验证 (CV) 的新方法. 这种方法使得复杂模型的预测性绩效评估在计算上可行,在不重复密集计算的情况下提供准确的估计.

关键词:
贝叶斯的等级回归模型是贝叶斯的等级回归模型.离开 - 集群 - 离开留下一个-外出一个.一个多层次的模型模型.插入估计器的插件

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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相关实验视频

Last Updated: Sep 12, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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科学领域:

  • 统计建模 统计建模
  • 计算统计学 计算统计学

背景情况:

  • 贝叶斯层次回归模型 (BHRMs) 广泛用于复杂的数据结构.
  • BHRM的计算成本往往禁止用于性能评估的标准交叉验证 (CV).

研究的目的:

  • 开发一种计算效率高的方法,用于获得BHRMs的交叉验证预测估计.
  • 让CV成为评估大型和复杂BHRM预测性能的实用工具.

主要方法:

  • 一种新的程序,通过对方差-协方差参数进行条件化,将CV问题重新定义为优化任务.
  • 该方法为留下一个-out CV和留下一个-cluster-out CV提供了近似值.

主要成果:

  • 拟议的方法大大降低了BHRM的CV的计算负担.
  • 在许多场景中,大致的CV估计被证明相当于完整的CV估计.
  • 该方法的有效性通过理论结果,模拟和现实数据分析来证明.

结论:

  • 这一新程序使得交叉验证成为评估贝叶斯分层回归模型的可行和可靠方法.
  • 该方法在复杂的统计建模中促进了更强大的模型选择和性能评估.