Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high‐throughput experiments-Reference-Cited by-同舟云学术

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Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high‐throughput experiments

Published:2022-09-19 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Zhang Feipeng¹,Li Qunhua²^ORCID

Affiliation:

1. School of Economics and Finance Xi'an Jiaotong University Xi'an China

2. Department of Statistics Pennsylvania State University Pennsylvania USA

Funder

National Institute of General Medical Sciences

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13757

Reference26 articles.

1. Tests for Parameter Instability and Structural Change With Unknown Change Point

2. Testing for Parameter Constancy in Linear Regressions: An Empirical Distribution Function Approach

3. Consistency and Limiting Distribution of the Least Squares Estimator of a Threshold Autoregressive Model

4. Analysis of least absolute deviation

5. Systematic evaluation of factors influencing ChIP-seq fidelity

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