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Evaluation of reproducibility for paired functional data

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  • Li, Runze
  • Chow, Mosuk

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.

Suggested Citation

  • Li, Runze & Chow, Mosuk, 2005. "Evaluation of reproducibility for paired functional data," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 81-101, March.
    • Handle: RePEc:eee:jmvana:v:93:y:2005:i:1:p:81-101
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    References listed on IDEAS

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    1. Lin L. & Hedayat A. S. & Sinha B. & Yang M., 2002. "Statistical Methods in Assessing Agreement: Models, Issues, and Tools," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 257-270, March.
    2. Huiman X. Barnhart & John M. Williamson, 2001. "Modeling Concordance Correlation via GEE to Evaluate Reproducibility," Biometrics, The International Biometric Society, vol. 57(3), pages 931-940, September.
    3. J. Fan & J.‐T. Zhang, 2000. "Two‐step estimation of functional linear models with applications to longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 303-322.
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    2. John J. Chen & Guangxiang Zhang & Chen Ji & George F. Steinhardt, 2011. "Simple moment-based inferences of generalized concordance correlation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1867-1882, October.
    3. Kuhnt, Sonja & Rehage, André, 2016. "An angle-based multivariate functional pseudo-depth for shape outlier detection," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 325-340.
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    7. Valencia García, Dalia Jazmin & Lillo Rodríguez, Rosa Elvira & Romo, Juan, 2013. "Spearman coefficient for functions," DES - Working Papers. Statistics and Econometrics. WS ws133329, Universidad Carlos III de Madrid. Departamento de Estadística.

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