Machine learning and other gibberish
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
#statistics
repository compiled of seminal papers in the field of statistics
https://ledaliang.github.io/journalclub/
repository compiled of seminal papers in the field of statistics
https://ledaliang.github.io/journalclub/
#ml #statistics
I read about conformal prediction a while ago and realized that I need to understand more about the hypothesis testing theories. As someone from natural science, I mostly work within the Neyman-Pearson ideas.
So I explored it a bit and found two nice papers. See the list below. If you have other papers on similar topics, I would appreciate some comments.
1. Perezgonzalez JD. Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing. Front Psychol. 2015;6: 223. doi:10.3389/fpsyg.2015.00223 https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00223/full
2. Lehmann EL. The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? J Am Stat Assoc. 1993;88: 1242–1249. doi:10.2307/2291263
I read about conformal prediction a while ago and realized that I need to understand more about the hypothesis testing theories. As someone from natural science, I mostly work within the Neyman-Pearson ideas.
So I explored it a bit and found two nice papers. See the list below. If you have other papers on similar topics, I would appreciate some comments.
1. Perezgonzalez JD. Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing. Front Psychol. 2015;6: 223. doi:10.3389/fpsyg.2015.00223 https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00223/full
2. Lehmann EL. The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? J Am Stat Assoc. 1993;88: 1242–1249. doi:10.2307/2291263
#statistics
This is the original paper of Fraser information.
Fisher information measures the second moment of the model sensitivity; Shannon information measures compressed information or variation of the information; Kullback (aka KL divergence) distinguishes two distributions.
Instead of defining a measure of information for different conditions, Fraser tweaked the Shannon information slightly and made it more generic. The Fraser information can be reduced to Fisher information, Shannon information, and Kullback information under certain conditions.
It is such a simple yet powerful idea.
Fraser DAS. On Information in Statistics. aoms. 1965;36: 890–896. doi:10.1214/aoms/1177700061
https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-36/issue-3/On-Information-in-Statistics/10.1214/aoms/1177700061.full
This is the original paper of Fraser information.
Fisher information measures the second moment of the model sensitivity; Shannon information measures compressed information or variation of the information; Kullback (aka KL divergence) distinguishes two distributions.
Instead of defining a measure of information for different conditions, Fraser tweaked the Shannon information slightly and made it more generic. The Fraser information can be reduced to Fisher information, Shannon information, and Kullback information under certain conditions.
It is such a simple yet powerful idea.
Fraser DAS. On Information in Statistics. aoms. 1965;36: 890–896. doi:10.1214/aoms/1177700061
https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-36/issue-3/On-Information-in-Statistics/10.1214/aoms/1177700061.full
#statistics
https://en.wikipedia.org/wiki/Dependent_and_independent_variables#Statistics_synonyms
The broken jargon system in statistics ...😱
> Depending on the context, an independent variable is sometimes called a "predictor variable", regressor, covariate, "controlled variable", "manipulated variable", "explanatory variable", exposure variable (see reliability theory), "risk factor" (see medical statistics), "feature" (in machine learning and pattern recognition) or "input variable." In econometrics, the term "control variable" is usually used instead of "covariate".
https://en.wikipedia.org/wiki/Dependent_and_independent_variables#Statistics_synonyms
The broken jargon system in statistics ...😱
> Depending on the context, an independent variable is sometimes called a "predictor variable", regressor, covariate, "controlled variable", "manipulated variable", "explanatory variable", exposure variable (see reliability theory), "risk factor" (see medical statistics), "feature" (in machine learning and pattern recognition) or "input variable." In econometrics, the term "control variable" is usually used instead of "covariate".
