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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
Sampling Plans01:23

Sampling Plans

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...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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...
Convenience Sampling Method00:55

Convenience Sampling Method

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. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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

Published on: December 10, 2012

Bayesian geostatistical modelling with informative sampling locations.

D Pati1, B J Reich, D B Dunson

  • 1Department of Statistical Science, Duke University, 214 Old Chemistry Building, Durham, North Carolina 27708-0251, U.S.A. , dp55@stat.duke.edu.

Biometrika
|August 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian geostatistical model to analyze how data collection locations influence outcomes. The findings confirm that sampling locations significantly impact results, a crucial insight for environmental data analysis.

Keywords:
Cox processGaussian processJoint modelPoint patternPosterior consistencyPreferential sampling

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Area of Science:

  • Environmental statistics
  • Geostatistics
  • Bayesian inference

Background:

  • Traditional geostatistical models often assume data collection locations are independent of outcome variables.
  • Understanding the influence of sampling design on data is critical for accurate spatial analysis.
  • Informative sampling can bias results if not properly accounted for.

Purpose of the Study:

  • To develop a geostatistical framework that explicitly models informative sampling.
  • To assess the impact of data collection locations on outcome variables.
  • To provide a robust method for analyzing spatial data where location is not random.

Main Methods:

  • A Bayesian approach combining log Gaussian Cox processes for location modeling and Gaussian processes for outcome modeling.
  • Modeling outcomes conditionally on locations, incorporating spatial random effects.
  • Adjusting for the location intensity process to account for non-random sampling.

Main Results:

  • Demonstrated posterior propriety under an improper prior, confirming the model's validity.
  • Established consistent estimation of location density and outcome mean functions under mild assumptions.
  • Showed significant evidence of informative sampling in ozone data from the Eastern U.S.A.

Conclusions:

  • The proposed Bayesian geostatistical model effectively detects and quantifies informative sampling.
  • The methodology allows for more accurate spatial predictions by accounting for sampling biases.
  • This approach enhances the reliability of environmental monitoring and statistical inference.