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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Bootstrapping01:24

Bootstrapping

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 small or...
Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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(point estimate - error bound, point estimate + error bound)
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
Survival Tree01:19

Survival Tree

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.
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Systematic 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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
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Predicting the required number of training samples.

H M Kalayeh1, D A Landgrebe

  • 1Object Recognition Systems, Inc., Princeton, NJ 08540.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

A new criterion assesses covariance matrix estimation quality for multivariate normal distributions. This method predicts the required number of training samples, aiding experimental design.

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Area of Science:

  • Statistics
  • Multivariate Analysis
  • Statistical Modeling

Background:

  • Accurate estimation of the covariance matrix is crucial for multivariate statistical analysis.
  • The quality of covariance matrix estimates depends heavily on the number of available training samples.
  • Determining the optimal number of samples is essential for reliable statistical inference.

Purpose of the Study:

  • To develop a novel criterion for evaluating the quality of covariance matrix estimates.
  • To establish a method for predicting the necessary number of training samples based on this criterion.
  • To provide experimental validation for the proposed sample size prediction method.

Main Methods:

  • Development of a quality assessment criterion for covariance matrix estimation.
  • Application of the criterion to predict the required number of training samples.
  • Conducting experimental studies to validate the prediction methodology.

Main Results:

  • A quantifiable criterion for covariance matrix estimation quality has been established.
  • The study successfully predicts the number of training samples needed for a desired estimation quality.
  • Experimental results confirm the utility of the criterion in guiding sample size determination.

Conclusions:

  • The developed criterion offers a robust method for assessing covariance matrix estimation quality.
  • The prediction of training sample size based on this criterion is a valuable tool for researchers.
  • This work provides practical guidance for experimental design in multivariate statistical studies.