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Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence.

Byungsoo Kim¹, Sangyeol Lee²

¹Department of Statistics, Yeungnam University, Gyeongsan 38541, Korea.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This summary is machine-generated.

This study introduces a robust change point test for integer-valued time series data, even with outliers. The proposed method, using density power divergence, proves effective and reliable in detecting parameter shifts.

Keywords:

density power divergence integer-valued time series minimum density power divergence estimator one-parameter exponential family robust change point test

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Area of Science:

Time Series Analysis
Statistical Inference
Robust Statistics

Background:

Parameter change detection is crucial in time series analysis.
Traditional methods are sensitive to outliers in data.
Integer-valued time series models are common in various fields.

Purpose of the Study:

To develop a robust test for parameter change detection in integer-valued time series.
To address the challenge of data contamination by outliers.
To utilize density power divergence for robust estimation and testing.

Main Methods:

Employing a robust change point test based on density power divergence (DPD).
Utilizing the minimum density power divergence estimator (MDPDE) as the objective function.
Deriving the limiting null distribution of the DPD-based test.

Main Results:

The DPD-based test exhibits robustness against outliers.
The limiting null distribution of the test is a function of a Brownian bridge.
Monte Carlo simulations confirm the test's reliable performance.

Conclusions:

The proposed DPD-based test is effective for detecting parameter changes in contaminated integer-valued time series.
The test demonstrates robust properties inherited from MDPDE and DPD.
The methodology is validated through a real-world financial time series analysis.