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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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Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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Sampling Plans01:23

Sampling Plans

888
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
888
Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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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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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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関連する実験動画

Updated: Jan 13, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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隠れた集団の推定における回答者駆動サンプリングの可視性分布モデリングとその応用

Katherine R McLaughlin1, Lisa G Johnston2, Xhevat Jakupi3

  • 1Department of Statistics, Oregon State University.

The annals of applied statistics
|January 7, 2026
PubMed
まとめ

回答者駆動サンプリング（RDS）は、新しい「可視性」モデルを使用することで、隠れた集団の推定を改善できます。このアプローチは、自己申告によるネットワークサイズの偏りに対応し、集団サイズと有病率の推定値を向上させます。

キーワード:
ヒープデータ隠れた集団測定誤差モデルモデルベースの調査サンプリングネットワークサンプリング

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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科学分野:

  • 統計学
  • 疫学
  • ソーシャルネットワーク分析

背景:

  • 回答者駆動サンプリング（RDS）は、隠れた集団の研究に不可欠ですが、偏りやすい自己申告によるネットワークサイズに依存しています。
  • 現在のRDS推定値は、自己申告によるネットワークサイズ（次数）を使用して確率を近似しており、潜在的な不正確さにつながります。

研究 の 目的:

  • RDSデータの成功的サンプリング人口推定（SS-PSE）フレームワークを強化すること。
  • 信頼性の低い自己申告によるネットワークサイズに代わる「可視性」測定誤差モデルを導入すること。
  • RDSからの人口サイズおよび有病率推定の精度を向上させること。

主な方法:

  • 参加者の「可視性」のための測定誤差モデルを組み込んだ強化されたSS-PSEフレームワークを開発しました。
  • 参加者が募集できる個人数をモデル化しました。
  • コソボの3つの集団からのRDSデータに可視性SS-PSEフレームワークを適用しました。

主要な成果:

  • 可視性モデルは、度数分布を効果的に平滑化し、欠損/無効なネットワークサイズデータを処理します。
  • 強化されたSS-PSEフレームワークのパフォーマンスを実際のRDSデータで実証しました。
  • 推論された可視性は、自己申告によるネットワークサイズよりも堅牢な尺度を提供します。

結論:

  • 提案された可視性モデリングフレームワークは、RDSの従来のSS-PSE法よりも大幅な改善を提供します。
  • このアプローチは、隠れた集団の研究における自己申告によるネットワークサイズに関連する偏りを軽減できます。
  • このフレームワークは、将来の研究における有病率推定への拡張の可能性を示しています。