Abstract

Evaluation of reproducibility is important in assessing whether a new method or instrument can reproduce the results from a traditional gold standard approach. In this paper, we propose a measure to assess measurement agreement for functional data which are frequently encountered in medical research and many other research fields. Formulae to compute the standard error of the proposed estimator and confidence intervals for the proposed measure are derived. The estimators and the coverage probabilities of the confidence intervals are empirically tested for small-to-moderate sample sizes via Monte Carlo simulations. A real data example in physiology study is used to illustrate the proposed statistical inference procedures.

Original languageEnglish (US)
Pages (from-to)81-101
Number of pages21
JournalJournal of Multivariate Analysis
Volume93
Issue number1
DOIs
StatePublished - Mar 1 2005

Fingerprint

Paired Data
Functional Data
Reproducibility
Confidence interval
Estimator
Physiology
Coverage Probability
Evaluation
Standard error
Statistical Inference
Gold
Sample Size
Monte Carlo Simulation
Standards
Sample size
Field research
Gold standard
Statistical inference
Monte Carlo simulation
Medical research

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Cite this

@article{de3a5eddabea4e579178d50ab82ca405,
title = "Evaluation of reproducibility for paired functional data",
abstract = "Evaluation of reproducibility is important in assessing whether a new method or instrument can reproduce the results from a traditional gold standard approach. In this paper, we propose a measure to assess measurement agreement for functional data which are frequently encountered in medical research and many other research fields. Formulae to compute the standard error of the proposed estimator and confidence intervals for the proposed measure are derived. The estimators and the coverage probabilities of the confidence intervals are empirically tested for small-to-moderate sample sizes via Monte Carlo simulations. A real data example in physiology study is used to illustrate the proposed statistical inference procedures.",
author = "Runze Li and Mosuk Chow",
year = "2005",
month = "3",
day = "1",
doi = "10.1016/j.jmva.2004.01.010",
language = "English (US)",
volume = "93",
pages = "81--101",
journal = "Journal of Multivariate Analysis",
issn = "0047-259X",
publisher = "Academic Press Inc.",
number = "1",

}

Evaluation of reproducibility for paired functional data. / Li, Runze; Chow, Mosuk.

In: Journal of Multivariate Analysis, Vol. 93, No. 1, 01.03.2005, p. 81-101.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Evaluation of reproducibility for paired functional data

AU - Li, Runze

AU - Chow, Mosuk

PY - 2005/3/1

Y1 - 2005/3/1

N2 - Evaluation of reproducibility is important in assessing whether a new method or instrument can reproduce the results from a traditional gold standard approach. In this paper, we propose a measure to assess measurement agreement for functional data which are frequently encountered in medical research and many other research fields. Formulae to compute the standard error of the proposed estimator and confidence intervals for the proposed measure are derived. The estimators and the coverage probabilities of the confidence intervals are empirically tested for small-to-moderate sample sizes via Monte Carlo simulations. A real data example in physiology study is used to illustrate the proposed statistical inference procedures.

AB - Evaluation of reproducibility is important in assessing whether a new method or instrument can reproduce the results from a traditional gold standard approach. In this paper, we propose a measure to assess measurement agreement for functional data which are frequently encountered in medical research and many other research fields. Formulae to compute the standard error of the proposed estimator and confidence intervals for the proposed measure are derived. The estimators and the coverage probabilities of the confidence intervals are empirically tested for small-to-moderate sample sizes via Monte Carlo simulations. A real data example in physiology study is used to illustrate the proposed statistical inference procedures.

UR - http://www.scopus.com/inward/record.url?scp=7044251915&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=7044251915&partnerID=8YFLogxK

U2 - 10.1016/j.jmva.2004.01.010

DO - 10.1016/j.jmva.2004.01.010

M3 - Article

VL - 93

SP - 81

EP - 101

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

SN - 0047-259X

IS - 1

ER -