# SOCR EduMaterials Activities General CI Experiment

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 Revision as of 18:18, 7 July 2009 (view source)IvoDinov (Talk | contribs)← Older edit Revision as of 18:23, 7 July 2009 (view source)IvoDinov (Talk | contribs) (→Summary)Newer edit → Line 4: Line 4: There are two types of parameter estimates – ''point-based'' and ''interval-based'' estimates. Point-estimates refer to unique quantitative estimates of various parameters. Interval-estimates represent ranges of plausible values for the parameters of interest. There are different algorithmic approaches, prior assumptions and principals for computing data-driven parameter estimates. Both point and interval estimates depend on the distribution of the process of interest, the available computational resources and other criteria that may be desirable (Stewarty 1999) – e.g., [http://en.wikipedia.org/wiki/Bias_of_an_estimator biasness] and [http://en.wikipedia.org/wiki/Robust_statistics robustness] of the estimates. Accurate, robust and efficient parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. There are two types of parameter estimates – ''point-based'' and ''interval-based'' estimates. Point-estimates refer to unique quantitative estimates of various parameters. Interval-estimates represent ranges of plausible values for the parameters of interest. There are different algorithmic approaches, prior assumptions and principals for computing data-driven parameter estimates. Both point and interval estimates depend on the distribution of the process of interest, the available computational resources and other criteria that may be desirable (Stewarty 1999) – e.g., [http://en.wikipedia.org/wiki/Bias_of_an_estimator biasness] and [http://en.wikipedia.org/wiki/Robust_statistics robustness] of the estimates. Accurate, robust and efficient parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. - This activity demonstrates the usage and functionality of [http://socr.ucla.edu/htmls/exp/Confidence_Interval_Experiment_General.html SOCR General Confidence Interval Applet]. This applet is complementary to the [http://socr.ucla.edu/htmls/exp/Confidence_Interval_Experiment.html SOCR Simple Confidence Interval Applet] and its corresponding [[SOCR_EduMaterials_Activities_CoinfidenceIntervalExperiment | activity]]. + This activity demonstrates the usage and functionality of [http://socr.ucla.edu/htmls/exp/Confidence_Interval_Experiment_General.html SOCR General Confidence Interval Applet]. This applet is complementary to the [http://socr.ucla.edu/htmls/exp/Confidence_Interval_Experiment.html SOCR Simple Confidence Interval Applet] and its [[SOCR_EduMaterials_Activities_CoinfidenceIntervalExperiment | corresponding activity]]. ==Goals== ==Goals==

## Summary

There are two types of parameter estimates – point-based and interval-based estimates. Point-estimates refer to unique quantitative estimates of various parameters. Interval-estimates represent ranges of plausible values for the parameters of interest. There are different algorithmic approaches, prior assumptions and principals for computing data-driven parameter estimates. Both point and interval estimates depend on the distribution of the process of interest, the available computational resources and other criteria that may be desirable (Stewarty 1999) – e.g., biasness and robustness of the estimates. Accurate, robust and efficient parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors.

This activity demonstrates the usage and functionality of SOCR General Confidence Interval Applet. This applet is complementary to the SOCR Simple Confidence Interval Applet and its corresponding activity.

## Goals

The aims of this activity is to:

• TBD
• TBD
• TBD.

## Motivational example

A 2005 study proposing a new computational brain atlas for Alzheimer’s disease (Mega et al., 2005) investigated the mean volumetric characteristics and the spectra of shapes and sizes of different cortical and subcortical brain regions for Alzheimer’s patients, individuals with minor cognitive impairment and asymptomatic subjects. This study estimated a number of centrality and variability parameters for these thee populations. Based on these point- and interval-estimates, the study analyzed a number of digital scans to derive criteria for imaging-based classification of subjects based on the intensities of their 3D brain scans. Their results enabled a number of subsequent inference studies that quantified the effects of subject demographics (e.g., education level, familial history, APOE allele, etc.), stage of the disease and the efficacy of new drug treatments targeting Alzheimer’s disease. The Figure to the right illustrates the shape, center and distribution parameters for the 3D geometric structure of the right hippocampus in the Alzheimer’s disease brain atlas. New imaging data can then be coregistered and compared relative to the amount of anatomical variability encoded in this atlas. This enables automated, efficient and quantitative inference on large number of brain volumes. Examples of point and interval estimates computed in this atlas framework include the mean-intensity and mean shape location, and the standard deviation of intensities and the mean deviation of shape, respectively.

## Activity

TBD!

In addition to this activity, open 2 more browser tabs - one pointing to the SOCR General CI applet and the other displaying the SOCR (WHICH ONE?) dataset. The images below show the arrangement of these 3 browser tabs.

• List the steps ....

TBD

TBDn.

## References

• Mega, M., Dinov, I., Thompson, P., Manese, M., Lindshield, C., Moussai, J., Tran, N., Olsen, K., Felix, J., Zoumalan, C., Woods, R., Toga, A., and Mazziotta, J. (2005). Automated brain tissue assessment in the elderly and demented population: Construction and validation of a sub-volume probabilistic brain atlas. NeuroImage, 26(4), 1009-1018.
• Stewarty, C. (1999). Robust Parameter Estimation in Computer Vision. SIAM Review, 41(3), 513–537.
• Wolfram, S. (2002). A New Kind of Science, Wolfram Media Inc.

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