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Statistics
Statistics
==Estimation==
===Maximum Likelihood Estimator===
(MLE)
===Uniformly Minimum Variance Unbiased Estimator===
UMVUE, sometimes called MVUE or UMVU.
==Tests==
===Basic Tests===
====T-test====
Used to test the mean.
====F-test====
Use to test the ratio of variances.
===Likelihood Ratio Test===
===Uniformly Most Powerful Test===
UMP Test
===Anova===
==Confidence Sets==
Confidence Intervals
===Relationship with Tests===
==Regression==
==Quadratic Forms==
==Bootstrapping==
[https://en.wikipedia.org/wiki/Bootstrapping_(statistics) Wikipedia]<br>
Boostrapping is used to sample from your sample to get a measure of accuracy of your statistics.
===Nonparametric Bootstrapping===
In nonparametric bootstrapping, you resample from your sample with replacement.<br>
In this scenario, you don't need to know the family of distributions that your sample comes from.
===Parametric Bootstrapping===
In parametric bootstrapping, you learn the distribution parameters of your sample, e.g. with MLE.<br>
Then you can generate samples from that distribution on a computer.
==Textbooks==
* [https://smile.amazon.com/Statistical-Inference-George-Casella/dp/0534243126?sa-no-redirect=1 Casella and Burger's Statistical Inference]

Revision as of 13:11, 12 November 2019

Statistics

Estimation

Maximum Likelihood Estimator

(MLE)

Uniformly Minimum Variance Unbiased Estimator

UMVUE, sometimes called MVUE or UMVU.

Tests

Basic Tests

T-test

Used to test the mean.

F-test

Use to test the ratio of variances.

Likelihood Ratio Test

Uniformly Most Powerful Test

UMP Test

Anova

Confidence Sets

Confidence Intervals

Relationship with Tests

Regression

Quadratic Forms

Bootstrapping

Wikipedia
Boostrapping is used to sample from your sample to get a measure of accuracy of your statistics.

Nonparametric Bootstrapping

In nonparametric bootstrapping, you resample from your sample with replacement.
In this scenario, you don't need to know the family of distributions that your sample comes from.

Parametric Bootstrapping

In parametric bootstrapping, you learn the distribution parameters of your sample, e.g. with MLE.
Then you can generate samples from that distribution on a computer.

Textbooks