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An R-Based Landscape Validation of a Competing Risk Model

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An evaluation of the bootstrap for model validation in mixture models.

Thomas Jaki¹, Ting-Li Su², Minjung Kim³

¹Lancaster University.

Communications in Statistics: Simulation and Computation

|December 12, 2018

Summary

The non-parametric bootstrap method is unreliable for validating mixture models, often failing to accurately identify the correct number of classes or ensure parameter stability. This statistical validation technique is therefore not recommended for mixture model analysis.

Keywords:

Finite mixture models leave-k-out cross-validation model validation nonparametric Bootstrap regression mixture models

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Area of Science:

Statistics
Machine Learning
Data Science

Background:

The non-parametric bootstrap is a widely used statistical technique for model validation.
Its application in validating mixture models, including finite mixture and regression mixture models, requires careful evaluation.

Purpose of the Study:

To assess the efficacy of the non-parametric bootstrap for validating mixture models.
To identify potential issues with using bootstrapping for class enumeration and parameter stability in mixture models.

Main Methods:

Simulations were conducted using finite mixture and regression mixture models.
The performance of the non-parametric bootstrap was evaluated under various conditions, including model violations.
Leave-k-out cross-validation was used as a comparative method.

Main Results:

Bootstrapping failed to detect the correct number of classes in 56% of simulations for a basic finite mixture model.
Performance was even poorer for regression mixture models and when model assumptions were violated.
The resampling nature of bootstrapping can lead to influential observations being over-represented, causing spurious class extraction.

Conclusions:

The non-parametric bootstrap is not recommended for validating mixture models due to its unreliability in class enumeration and parameter stability assessment.
Alternative methods like leave-k-out cross-validation, which sample without replacement, are more suitable for mixture model validation.