Minimize Chi-Square Estimation Algorithm

A Minimize Chi-Square Estimation Algorithm is an point estimation method that finds the values of parameters which make the chi-square test statistic as small as possible.

• Context:
  • It can be implemented by a Minimum Chi-Square Estimation System.
• Counter-Example(s):
  • a Maximize Likelihood Method.
• See: Degrees of Freedom (Statistics), Chi-Square Test, Chi-Square Distribution, Sample Size.

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References

2018

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Minimum_chi-square_estimation Retrieved:2018-3-7.
  • In statistics, minimum chi-square estimation is a method of estimation of unobserved quantities based on observed data.

    In certain chi-square tests, one rejects a null hypothesis about a population distribution if a specified test statistic is too large, when that statistic would have approximately a chi-square distribution if the null hypothesis is true. In minimum chi-square estimation, one finds the values of parameters that make that test statistic as small as possible.

    Among the consequences of its use is that the test statistic actually does have approximately a chi-square distribution when the sample size is large. Generally, one reduces by 1 the number of degrees of freedom for each parameter estimated by this method.

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