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Related Experiment Video

Updated: Jun 3, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

On the efficiency of bootstrap method into the analysis contingency table.

Saeid Amiri¹, Dietrich von Rosen

¹Department of Mathematics, Uppsala University, PO Box 480, 751 06 Uppsala, Sweden. saeid.amiri1@gmail.com

Computer Methods and Programs in Biomedicine

|April 5, 2011

Summary

This summary is machine-generated.

Nonparametric bootstrap tests improve contingency table analysis. These computer-intensive methods offer better performance than standard tests for categorical data, enhancing statistical inference.

Related Experiment Videos

Last Updated: Jun 3, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Statistics
Data Analysis

Background:

Contingency table analysis is crucial in applied fields.
Nonparametric inference often relies on computer-intensive methods like the bootstrap.
Existing research on bootstrap methods for contingency tables is limited.

Purpose of the Study:

To investigate the application and efficacy of nonparametric bootstrap tests for analyzing contingency tables.
To address the limitations of discrete p-values in standard contingency table tests.

Main Methods:

Utilizing the bootstrap method for nonparametric inference.
Developing and applying bootstrap versions of standard contingency table tests.
Employing Monte Carlo simulations to evaluate test properties.

Main Results:

Bootstrap versions of contingency table tests demonstrate superior performance compared to standard tests.
The proposed bootstrap tests provide more reliable p-values under the null hypothesis.
Simulations confirm the effectiveness and properties of the developed bootstrap tests.

Conclusions:

Nonparametric bootstrap tests offer an improved approach for categorical data analysis, particularly for contingency tables.
The study highlights the advantages of bootstrap methods in overcoming limitations of traditional statistical tests.
Further research is warranted to explore the full potential of these methods in various applied settings.