Discrete Optimization of Statistical Sample Sizes in Simulation by Using the Hierarchical Bootstrap Method

03/28/2013
by   A. Andronov, et al.
0

The Bootstrap method application in simulation supposes that value of random variables are not generated during the simulation process but extracted from available sample populations. In the case of Hierarchical Bootstrap the function of interest is calculated recurrently using the calculation tree. In the present paper we consider the optimization of sample sizes in each vertex of the calculation tree. The dynamic programming method is used for this aim. Proposed method allows to decrease a variance of system characteristic estimators.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/01/2020

Neighbourhood Bootstrap for Respondent-Driven Sampling

Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a s...
research
09/18/2022

A new approach to Statistical analysis of election results

In this paper, a new method of detection of election fraud is proposed. ...
research
06/17/2019

Efficient computation of the cumulative distribution function of a linear mixture of independent random variables

For a variant of the algorithm in [Pit19] (arXiv:1903.10816) to compute ...
research
02/09/2018

Bootstrap validation of links of a minimum spanning tree

We describe two different bootstrap methods applied to the detection of ...
research
02/24/2019

Snowboot: Bootstrap Methods for Network Inference

Complex networks are used to describe a broad range of disparate social ...
research
05/05/2018

Dynamic Monopolies in Reversible Bootstrap Percolation

We study an extremal question for the (reversible) r-bootstrap percolati...
research
08/01/2019

Estimating the Standard Error of Cross-Validation-Based Estimators of Classification Rules Performance

First, we analyze the variance of the Cross Validation (CV)-based estima...

Please sign up or login with your details

Forgot password? Click here to reset