Gauss and the identity function – a tale of characterizations of the normal distribution

03/03/2020
by   Christophe Ley, et al.
0

The normal distribution is well-known for several results that it is the only to fulfil. The aim of the present paper is to show that many of these characterizations actually follow from the fact that the derivative of the log-density of the normal distribution is the (negative) identity function. This a priori very simple yet surprising observation allows a deeper understanding of existing characterizations and paves the way to an immediate extension to a general density x p(x) by replacing -x in these results with (log p(x))'.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/19/2023

The information matrix of the bivariate extended skew-normal distribution

For the extended skew-normal distribution, which represents an extension...
research
08/06/2023

Asymptotic comparison of negative multinomial and multivariate normal experiments

This note presents a refined local approximation for the logarithm of th...
research
10/31/2018

Exceptionally Monadic Error Handling

We notice that the type of src_haskellcatch :: c a -> (e -> c a) -> c a ...
research
10/25/2022

Log normal claim models with common shocks

This paper is concerned with modelling multiple claim arrays that are su...
research
11/08/2019

Normal variance mixtures: Distribution, density and parameter estimation

Efficient computation of the distribution and log-density function of mu...
research
10/11/2019

Beta Rank Function: A Smooth Double-Pareto-Like Distribution

The Beta Rank Function (BRF) x(u) =A(1-u)^b/u^a, where u is the normaliz...

Please sign up or login with your details

Forgot password? Click here to reset