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

Content Provider	Europe PMC
Author	Wang, Xueying Zhou, Chunxiao Makambi, Kepher Yuan, Ao Ahn, Jaeil
Copyright Year	2019
Abstract	ABSTRACT Batched data is a type of data where each observed data value is the sum of a number of grouped (batched) latent ones obtained under different conditions. Batched data arises in various practical backgrounds and is often found in social studies and management sector. The analysis of such data is analytically challenging due to its structural complexity. In this article, we describe how to analyze batched service time data, estimate the mean and variance of each batch that are latent. We in particular focus on the situation when the observed total time includes an unknown proportion of non-service time. To address this problem, we propose a Gaussian model for efficiency as well as a semi-parametric kernel density model for robustness. We evaluate the performance of both proposed methods through simulation studies and then applied our methods to analyze a batched data.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9041598&blobtype=pdf
Page Count	17
ISSN	02664763
Volume Number	47
DOI	10.1080/02664763.2019.1645820
PubMed Central reference number	PMC9041598
Issue Number	3
PubMed reference number	35706964
Journal	Journal of Applied Statistics [J Appl Stat]
e-ISSN	13600532
Language	English
Publisher	Taylor & Francis
Publisher Date	2019-07-24
Access Restriction	Open
Rights License	© 2019 Informa UK Limited, trading as Taylor & Francis Group
Subject Keyword	Batched data latent observations Gaussian model kernel density estimator parametric method semi-parametric method 62Gxx 62Fxx
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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