A geometric approach to statistical estimation
Rudolf Kulhavý
Abstract
Rudolf Kulhavý
Abstract
The role of Kerridge inaccuracy, Shannon entropy and Kullback-Leibler distance in statistical estimation is shown for both discrete and continuous observations. The cases of data independence and regression-type dependence are considered in parallel. Pythagorean-like relations valid for probability distributions are presented and their importance for estimation under compressed data is indicated.
OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The role of Kerridge inaccuracy, Shannon entropy and Kullback-Leibler distance in statistical estimation is shown for both discrete and continuous observations. The cases of data independence and regression-type dependence are considered in parallel. Pythagorean-like relations valid for probability distributions are presented and their importance for estimation under compressed data is indicated.
Key concepts: Entropy (arrow of time), Independence (probability theory), Pythagorean theorem, Kullback–Leibler divergence, Mathematics, Estimation, Computer science, Probability distribution