Effective WLLN, SLLN and CLT in statistical models

Ryszard Zieliński

Applicationes Mathematicae (2004)

  • Volume: 31, Issue: 1, page 117-125
  • ISSN: 1233-7234

Abstract

top
Weak laws of large numbers (WLLN), strong laws of large numbers (SLLN), and central limit theorems (CLT) in statistical models differ from those in probability theory in that they should hold uniformly in the family of distributions specified by the model. If a limit law states that for every ε > 0 there exists N such that for all n > N the inequalities |ξₙ| < ε are satisfied and N = N(ε) is explicitly given then we call the law effective. It is trivial to obtain an effective statistical version of WLLN in the Bernoulli scheme, to get SLLN takes a little while, but CLT does not hold uniformly. Other statistical schemes are also considered.

How to cite

top

Ryszard Zieliński. "Effective WLLN, SLLN and CLT in statistical models." Applicationes Mathematicae 31.1 (2004): 117-125. <http://eudml.org/doc/279365>.

@article{RyszardZieliński2004,
abstract = {Weak laws of large numbers (WLLN), strong laws of large numbers (SLLN), and central limit theorems (CLT) in statistical models differ from those in probability theory in that they should hold uniformly in the family of distributions specified by the model. If a limit law states that for every ε > 0 there exists N such that for all n > N the inequalities |ξₙ| < ε are satisfied and N = N(ε) is explicitly given then we call the law effective. It is trivial to obtain an effective statistical version of WLLN in the Bernoulli scheme, to get SLLN takes a little while, but CLT does not hold uniformly. Other statistical schemes are also considered.},
author = {Ryszard Zieliński},
journal = {Applicationes Mathematicae},
keywords = {weak laws of large numbers; strong laws of large numbers; central limit theorems; statistical models; effective limit laws; uniform limit laws; Bernoulli scheme; exponential distribution; quantiles},
language = {eng},
number = {1},
pages = {117-125},
title = {Effective WLLN, SLLN and CLT in statistical models},
url = {http://eudml.org/doc/279365},
volume = {31},
year = {2004},
}

TY - JOUR
AU - Ryszard Zieliński
TI - Effective WLLN, SLLN and CLT in statistical models
JO - Applicationes Mathematicae
PY - 2004
VL - 31
IS - 1
SP - 117
EP - 125
AB - Weak laws of large numbers (WLLN), strong laws of large numbers (SLLN), and central limit theorems (CLT) in statistical models differ from those in probability theory in that they should hold uniformly in the family of distributions specified by the model. If a limit law states that for every ε > 0 there exists N such that for all n > N the inequalities |ξₙ| < ε are satisfied and N = N(ε) is explicitly given then we call the law effective. It is trivial to obtain an effective statistical version of WLLN in the Bernoulli scheme, to get SLLN takes a little while, but CLT does not hold uniformly. Other statistical schemes are also considered.
LA - eng
KW - weak laws of large numbers; strong laws of large numbers; central limit theorems; statistical models; effective limit laws; uniform limit laws; Bernoulli scheme; exponential distribution; quantiles
UR - http://eudml.org/doc/279365
ER -

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.