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Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

Decision Making: Traditional Method

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Decision Making

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Related Experiment Video

Updated: May 29, 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

Recursive implementation of a two-step nonparametric decision rule.

¹Computer Science Section, Wayne State University, Detroit, MI 48202; Department of Computer Science, State University of New York at Buffalo, Amherst, NY 14226.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces a recursive algorithm for nonparametric discrimination, improving computational efficiency and overcoming precision limitations in decision rules. The new method offers a more practical approach compared to traditional two-step density estimation techniques.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Computer Science
Statistics
Machine Learning

Background:

Nonparametric discrimination relies on estimating class-conditional densities.
Direct implementation of Bayes decision rules faces computational complexity and finite precision issues.
Existing methods struggle with increasing sample sizes and limited word-length.

Purpose of the Study:

To develop a recursive algorithm for nonparametric discrimination.
To address computational challenges in direct Bayes decision rule implementation.
To reduce operational complexity and mitigate word-length limitations.

Main Methods:

Developed a novel recursive algorithm for density estimation and decision rule derivation.
Analyzed computational complexity and precision constraints of the new algorithm.
Investigated a special case reducing to the weighted k-nearest-neighbor rule.

Main Results:

The recursive algorithm significantly reduces expected operations compared to direct methods.
The approach effectively handles finite precision limitations in decision rule domains.
Demonstrated a reduction in computational complexity and improved practical applicability.

Conclusions:

The proposed recursive algorithm offers a computationally efficient solution for nonparametric discrimination.
This method overcomes key limitations of traditional two-step approaches.
The algorithm provides a valuable alternative, particularly for large datasets and resource-constrained environments.