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Analysis of batched service time data using Gaussian and semi-parametric kernel models.

Xueying Wang1, Chunxiao Zhou2, Kepher Makambi1

  • 1Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC, USA.

Journal of Applied Statistics
|June 16, 2022
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Summary

This study introduces novel methods for analyzing batched data, specifically focusing on estimating latent service times and accounting for non-service time. The proposed Gaussian and kernel density models offer efficient and robust solutions for complex data analysis challenges.

Keywords:
62Fxx62GxxBatched dataGaussian modelkernel density estimatorlatent observationsparametric methodsemi-parametric method

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Area of Science:

  • Statistics
  • Data Analysis

Background:

  • Batched data, where observed values are sums of latent grouped data, is common in social studies and management.
  • Analyzing batched data is complex due to its inherent structure.

Purpose of the Study:

  • To develop methods for analyzing batched service time data.
  • To estimate latent mean and variance within batches.
  • To address the challenge of unknown non-service time within observed total times.

Main Methods:

  • Proposed a Gaussian model for efficient analysis.
  • Developed a semi-parametric kernel density model for robust analysis.
  • Evaluated methods via simulation studies.

Main Results:

  • Both Gaussian and kernel density models demonstrated effectiveness in analyzing batched service time data.
  • The methods successfully estimated latent batch means and variances.
  • The models accounted for the presence of non-service time.

Conclusions:

  • The proposed Gaussian and kernel density models provide viable solutions for analyzing complex batched data.
  • These methods enhance the understanding of service times in practical applications.
  • The study contributes robust analytical tools for batched data scenarios.