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相关概念视频

Upsampling01:22

Upsampling

571
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
571
Sampling Methods: Overview01:06

Sampling Methods: Overview

2.1K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
2.1K
Downsampling01:20

Downsampling

590
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
590
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

2.0K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
2.0K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

675
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
675
Bootstrapping01:24

Bootstrapping

794
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
794

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Updated: Jan 11, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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使用多次归算数据的重新采样方法.

Michael W Robbins1, Lane Burgette2

  • 1RAND Corporation, 4570 Fifth Avenue #600, Pittsburgh, Pennsylvania 15213, USA.

Biometrika
|November 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究阐明了使用重新采样方法,如用随机归纳的jackknife和bootstrap. 它表明,归算必须在复制体内独立,数据集的数量因重新采样类型而异,以准确地估计不确定性.

关键词:
在 Bootstrap 中使用 Bootstrap.这是一把大刀,一把大刀.缺少的数据数据.多重的归咎是多重的归咎.

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Author Spotlight: High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution
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相关实验视频

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Author Spotlight: High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution
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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:

  • 重复采样技术 (大刀,引导) 对于不确定性估计至关重要.
  • 缺失的数据很常见,通常通过归算处理,包括多重归算.
  • 将重新抽样与随机归纳结合起来,会带来独特的理论挑战.

研究的目的:

  • 为了推导出使用卡刀和靴带与随机归算的理论.
  • 为正确实施这些方法提供指导.
  • 为了解决复制组和归算数据集之间的相互作用.

主要方法:

  • 对于随机归算的重新采样规则的理论推导.
  • 在多重归算下分析刀和引导式方法.
  • 讨论偏差调整的刀和引导方法.
  • 模拟研究以验证理论发现.

主要成果:

  • 在每个复制组内,必须独立地为jackknife和bootstrap生成输入.
  • 杰克刀需要比复制组要多得多的归算数据集.
  • 对于相对于复制组的归算数据集的数量,Bootstrap没有同样严格的要求.
  • 偏差调整的方法可以减少对众多归算数据集的需求.

结论:

  • 使用随机归算重新采样的正确应用需要仔细考虑归算生成和数据集数量.
  • 该理论框架阐明了在不完整的数据集中估计不确定性的最佳策略.
  • 研究结果为使用这些先进的统计方法的研究人员提供了实际指导.