Rollout designs for lump-sum data

This work studies rollout design problems with a focus of suitable choices of rollout rate under the standard Type I and Type II error probabilities control framework. The main challenge of rollout design is that data is often observed in a lump-sum manner from a spatio-temporal point of view: (1) temporally, only the sum of data in a given sliding time window can be observed; (2) spatially, there are two subgroups for the data at each time step: control and treatment, but one can only observe the total values instead of individual values from each subgroup. We develop rollout tests of lump-sum data under both fixed-sample-size and sequential settings, subject to the constraints on Type I and Type II error probabilities. Numerical studies are conducted to validate our theoretical results.

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Publication Details

Journal
Journal of Applied Statistics
Published
2024-12-13
DOI
https://doi.org/10.1080/02664763.2024.2440031
Primary Topic
Advanced Statistical Process Monitoring
Type
article
Field-Weighted Citation Impact
0.00

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article

Rollout designs for lump-sum data

Yajun Mei, Hongzhen Tian, Anup Kumar Sarkar, Qinkai Xu
Journal of Applied Statistics
Advanced Statistical Process Monitoring
article

Rollout designs for lump-sum data

Yajun Mei, Hongzhen Tian, Anup Kumar Sarkar, Qinkai Xu
article en

Abstract

This work studies rollout design problems with a focus of suitable choices of rollout rate under the standard Type I and Type II error probabilities control framework. The main challenge of rollout design is that data is often observed in a lump-sum manner from a spatio-temporal point of view: (1) temporally, only the sum of data in a given sliding time window can be observed; (2) spatially, there are two subgroups for the data at each time step: control and treatment, but one can only observe the total values instead of individual values from each subgroup. We develop rollout tests of lump-sum data under both fixed-sample-size and sequential settings, subject to the constraints on Type I and Type II error probabilities. Numerical studies are conducted to validate our theoretical results.

Journal of Applied StatisticsVol. 52(9)
Microsoft (United States) (US), Microsoft Research (United Kingdom) (GB), New York University (US)
National Science Foundation
Openalex Percentile: Top 77%
Advanced Statistical Process Monitoring
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