IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v93y2005i1p81-101.html
   My bibliography  Save this article

Evaluation of reproducibility for paired functional data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(04)00017-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Valenzuela, Jose Maria & Qi, Ye, 2012. "Framing energy efficiency and renewable energy policies: An international comparison between Mexico and China," Energy Policy, Elsevier, vol. 51(C), pages 128-137.
    2. Ying Cui & Limin Peng & Yijuan Hu & HuiChuan J. Lai, 2021. "Assessing the reproducibility of microbiome measurements based on concordance correlation coefficients," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1027-1048, August.
    3. 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.
    4. 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.
    5. repec:cte:wsrepe:ws133329 is not listed on IDEAS
    6. Felix Achana & Stavros Petrou & Kamran Khan & Amadou Gaye & Neena Modi, 2018. "A methodological framework for assessing agreement between cost-effectiveness outcomes estimated using alternative sources of data on treatment costs and effects for trial-based economic evaluations," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(1), pages 75-86, January.
    7. Christian Ritz & Jens C. Streibig, 2009. "Functional Regression Analysis of Fluorescence Curves," Biometrics, The International Biometric Society, vol. 65(2), pages 609-617, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Chia-Cheng & Barnhart, Huiman X., 2008. "Comparison of ICC and CCC for assessing agreement for data without and with replications," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 554-564, December.
    2. Wang, Qihua & Zhang, Riquan, 2009. "Statistical estimation in varying coefficient models with surrogate data and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2389-2405, November.
    3. Thiago de Paula Oliveira & John Hinde & Silvio Sandoval Zocchi, 2018. "Longitudinal Concordance Correlation Function Based on Variance Components: An Application in Fruit Color Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 233-254, June.
    4. Čížek, Pavel & Koo, Chao Hui, 2021. "Jump-preserving varying-coefficient models for nonlinear time series," Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
    5. Geenens, Gery, 2015. "Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 369-377.
    6. Tang Qingguo & Cheng Longsheng, 2008. "M-estimation and B-spline approximation for varying coefficient models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(7), pages 611-625.
    7. Nguyen Van, Phu & Azomahou, Theophile, 2007. "Nonlinearities and heterogeneity in environmental quality: An empirical analysis of deforestation," Journal of Development Economics, Elsevier, vol. 84(1), pages 291-309, September.
    8. Qiu, Jia & Li, Degao & You, Jinhong, 2015. "SCAD-penalized regression for varying-coefficient models with autoregressive errors," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 100-118.
    9. Helenowski Irene B & Vonesh Edward F & Demirtas Hakan & Rademaker Alfred W & Ananthanarayanan Vijayalakshmi & Gann Peter H & Jovanovic Borko D, 2011. "Defining Reproducibility Statistics as a Function of the Spatial Covariance Structures in Biomarker Studies," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, January.
    10. Reiss Philip T. & Huang Lei & Mennes Maarten, 2010. "Fast Function-on-Scalar Regression with Penalized Basis Expansions," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-30, August.
    11. Yiqiang Lu & Riquan Zhang, 2009. "Smoothing spline estimation of generalised varying-coefficient mixed model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 815-825.
    12. Liao Jason J. Z. & Capen Robert, 2011. "An Improved Bland-Altman Method for Concordance Assessment," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-17, January.
    13. Zhang, Riquan & Li, Guoying, 2007. "Averaged estimation of functional-coefficient regression models with different smoothing variables," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 455-461, February.
    14. Hutson, Alan D., 2010. "A multi-rater nonparametric test of agreement and corresponding agreement plot," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 109-119, January.
    15. Peixin Zhao & Liugen Xue, 2009. "Empirical likelihood inferences for semiparametric varying-coefficient partially linear errors-in-variables models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 907-923.
    16. Qian Huang & Jinhong You & Liwen Zhang, 2022. "Efficient inference of longitudinal/functional data models with time‐varying additive structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 744-771, June.
    17. Zhaoping Hong & Yuao Hu & Heng Lian, 2013. "Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(7), pages 887-908, October.
    18. Lin, Cunjie & Zhou, Yong, 2014. "Analyzing right-censored and length-biased data with varying-coefficient transformation model," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 45-63.
    19. Chuang Wan & Wei Zhong & Wenyang Zhang & Changliang Zou, 2023. "Multikink quantile regression for longitudinal data with application to progesterone data analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 747-760, June.
    20. Zhang, Jun & Gai, Yujie & Wu, Ping, 2013. "Estimation in linear regression models with measurement errors subject to single-indexed distortion," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 103-120.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:93:y:2005:i:1:p:81-101. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.