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Finite-Sample Two-Group Composite Hypothesis Testing via Machine Learning

In the problem of composite hypothesis testing, identifying the potential uniformly most powerful (UMP) unbiased test is of great interest. Beyond typical hypothesis settings with exponential family, it is usually challenging to prove the existence and further construct such UMP unbiased tests with finite sample size. For example in the COVID-19 pandemic with limited previous assumptions on the treatment for investigation and the standard of care, adaptive clinical trials are appealing due to ethical considerations, and the ability to accommodate uncertainty while conducting the trial. Although several methods have been proposed to control Type I error rates, how to find a more powerful hypothesis testing strategy is still an open question. Motivated by this problem, we propose an automatic framework of constructing test statistics and corresponding critical values via machine learning methods to enhance power in a finite sample. In this article, we particularly illustrate the performance using Deep Neural Networks (DNN) and discuss its advantages. Simulations and two case studies of adaptive designs demonstrate that our method is automatic, general and prespecified to construct statistics with satisfactory power in finite-sample. Supplemental materials are available online including R code and an R shiny app.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.31 No.3 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 856-865

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

NEYMAN-PEARSON LEMMA
DEEP NEURAL NETWORKS
CONFIRMATORY ADAPTIVE CLINICAL TRIALS
EFFICIENT INFERENCE METHODS
RESEARCH ASSISTANT TOOLS

Info Detail Spesifik

https://doi.org/10.1080/10618600.2021.2020128

Pernyataan Tanggungjawab

Tianyu Zhan, Jian Kang

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