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相关概念视频

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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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
440
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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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...
8.1K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

70
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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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以二项式时间序列数据为基础的基于铜的马尔科夫链物流回归建模.

Pepi Novianti1,2, Gunardi1, Dedi Rosadi1

  • 1Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.

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|January 3, 2024
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概括

本研究介绍了一种基于的马尔科夫链逻辑回归模型,用于带有共变量的二项式时间序列数据. 最大概率估计 (MLE) 准确估计模型参数,揭示变量关系和时间依赖.

关键词:
非对称性属性 非对称性属性自动回归式 自动回归式克莱顿,条件概率的可能性.基于的马尔科夫链物流回归模型.计数时间序列时间序列.弗兰克 弗兰克 弗兰克贝尔 (Gumbel) 是一个叫贝尔 (Gumbel) 的孩子.最大的概率估计估计.

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科学领域:

  • 统计 统计 统计 统计
  • 时间序列分析时间序列分析
  • 统计建模 统计建模

背景情况:

  • 传统的时间序列模型经常与二项式数据和共变量包含作斗争.
  • 偶数函数提供了一种灵活的方式来建模联合分布和依赖关系.
  • 马尔科夫链模型捕获数据中的顺序依赖关系.

研究的目的:

  • 为二项式时间序列数据开发基于的马尔科夫链逻辑回归模型.
  • 将共变量纳入时间序列模型.
  • 使用最大概率估计 (MLE) 估计模型参数,包括后勤回归和形参数.

主要方法:

  • 利用基于的马尔科夫链方法来建模二项式时间序列.
  • 集成后勤回归用于用共变量建模成功概率.
  • 在参数估计中使用双变量函数 (克莱顿,冈贝尔,弗兰克) 和最大概率估计 (MLE).

主要成果:

  • MLE证明了对拟议模型的准确参数估计.
  • 该模型有效地捕捉了依赖和独立变量之间的关系.
  • 该模型成功估计了二项式时间序列数据的时间依赖性.

结论:

  • 基于的马尔科夫链逻辑回归模型是分析具有共变量的二项式时间序列的可行方法.
  • 在这种复杂的模型中,MLE是一种高效的参数估计方法.
  • 该模型提供了对变量关联和时间动态的洞察.