Explore >> Select a destination
|
You are here 
|
ryantolsma.com | ||
| | | | |
aakinshin.net
|
|
| | | | | I have already discussed the concept of the quantile absolute deviation in several previous posts. In this post, we derive the equation for the relative statistical efficiency of the quantile absolute deviation against the standard deviation under the norma... | |
| | | | |
www.randomservices.org
|
|
| | | | | [AI summary] The text presents a comprehensive overview of the beta-Bernoulli process and its related statistical properties. Key concepts include: 1) The Bayesian estimator of the probability parameter $ p $ based on Bernoulli trials, which is $ rac{a + Y_n}{a + b + n} $, where $ a $ and $ b $ are parameters of the beta distribution. 2) The stochastic process $ s{Z} = rac{a + Y_n}{a + b + n} $, which is a martingale and central to the theory of the beta-Bernoulli process. 3) The distribution of the trial number of the $ k $th success, $ V_k $, which follows a beta-negative binomial distribution. 4) The mean and variance of $ V_k $, derived using conditional expectations. 5) The connection between the beta distribution and the negative binomial distributi... | |
| | | | |
danieltakeshi.github.io
|
|
| | | | | In my STAT 210A class, we frequently have to deal with the minimum of asequence of independent, identically distributed (IID) random variables. Thishappens b... | |
| | | | |
almostsuremath.com
|
|
| | | The martingale property is strong enough to ensure that, under relatively weak conditions, we are guaranteed convergence of the processes as time goes to infinity. In a previous post, I used Doob's upcrossing inequality to show that, with probability one, discrete-time martingales will converge at infinity under the extra condition of $latex {L^1}&fg=000000$-boundedness. Here, I... | ||
