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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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A complete procedure to test a claim about population standard deviation or population variance is explained here.
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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Related Experiment Video

Updated: May 6, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

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Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread

Kirsten L Peterson1,2, Ruben Sanchez-Romero1, Ravi D Mill1

  • 1Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102.

Biorxiv : the Preprint Server for Biology
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

Regularized methods significantly improve functional connectivity (FC) reliability in brain imaging. Graphical lasso offers accurate, robust FC estimates, outperforming standard methods for brain network analysis.

Keywords:
Diffusion MRIIndividual differencesNetwork neuroscienceRegularizationStructural connectivityfMRI

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Network Analysis

Background:

  • Functional connectivity (FC) analysis using resting-state fMRI is crucial for understanding brain communication.
  • Standard pairwise correlation methods for FC can be confounded by indirect connections.
  • Unregularized partial correlation methods, while reducing confounding, suffer from low reliability.

Purpose of the Study:

  • To investigate whether adding regularization to partial correlation methods can improve the reliability and accuracy of functional connectivity (FC) estimates.
  • To compare the performance of regularized methods (graphical lasso, graphical ridge, principal component regression) against unregularized partial and pairwise correlation.

Main Methods:

  • Applied unregularized (pairwise correlation, partial correlation) and regularized (graphical lasso, graphical ridge, principal component regression) methods to resting-state fMRI data and simulated datasets.
  • Assessed reliability using between-session similarity and intraclass correlation.
  • Validated accuracy against structural connectivity and ground truth networks.

Main Results:

  • Regularization substantially improved FC reliability across all tested methods.
  • Regularized methods, particularly graphical lasso, yielded more accurate individual FC estimates compared to unregularized approaches.
  • Graphical lasso demonstrated robustness to noise, data quantity, and motion artifacts, common in fMRI.
  • Resting-state graphical lasso FC successfully predicted task activations and behavioral differences.

Conclusions:

  • Regularized methods, especially graphical lasso, offer a more reliable and accurate approach to estimating functional connectivity than standard pairwise correlation.
  • Graphical lasso overcomes the reliability limitations of unregularized partial correlation, providing valid estimates of unconfounded brain connectivity.
  • These findings support the use of regularized methods for advanced brain network analysis in neuroscience research.