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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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对于分布值共变量的直接贝叶斯线性回归.

Bohao Tang1, Sandipan Pramanik1, Yi Zhao2

  • 1Department of Biostatistics, Johns Hopkins University.

Electronic journal of statistics
|October 11, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了直接贝叶斯回归方法,用于分布值数据,绕过密度估计. 该方法提高了准确性,特别是在有限的重复测量时,提供了一种分析复杂共变量关系的新方法.

关键词:
贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语斯过程是高斯过程.分布回归回归的分布.最低限度 (minimax) 最低限度 (minimax) 是什么意思

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科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 在分布上的标量回归模型中,使用分布值的共变量计算标量结果.
  • 传统方法在回归之前通过重复测量来估计特定主体的密度.
  • 这种中间密度估计可能是低效的,容易出错,尤其是稀疏的数据.

研究的目的:

  • 提出直接线性标量分布回归方法.
  • 为了规避从重复测量中估计中间密度的需要.
  • 提供理论上的保证,并探索实际应用的扩展.

主要方法:

  • 在线性回归框架中直接使用重复测量作为共变量.
  • 在贝叶斯推理的回归函数之前使用高斯过程.
  • 实现封闭形式或结合贝叶斯更新.

主要成果:

  • 拟议的方法实现了回归函数的最佳估计误差边界.
  • 与需要密度估计的方法相比,它表现出更高的性能,特别是在很少重复测量的情况下.
  • 该模型不变于重复测量的顺序,并扩展到非i.i.d. 设置. 设置. 这些设置.

结论:

  • 使用重复测量直接建模分布值共变量是可行的和有效的.
  • 这种方法为传统方法提供了统计学上合理和计算效率高的替代方案.
  • 这项研究开创了贝叶斯回归的理论分析,使用分布值的共变量.