IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v7y2011i1n1.html
   My bibliography  Save this article

Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components

Author

Listed:
  • Zhang Saijuan

    (Texas A&M University)

  • Krebs-Smith Susan M.

    (National Cancer Institute)

  • Midthune Douglas

    (National Cancer Institute)

  • Perez Adriana

    (University of Texas School of Public Health)

  • Buckman Dennis W.

    (Information Management Services, Inc.)

  • Kipnis Victor

    (National Cancer Institute)

  • Freedman Laurence S.

    (Gertner Institute for Epidemiology and Public Health Research)

  • Dodd Kevin W.

    (National Cancer Institute)

  • Carroll Raymond J

    (Texas A&M University)

Abstract

There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.

Suggested Citation

  • Zhang Saijuan & Krebs-Smith Susan M. & Midthune Douglas & Perez Adriana & Buckman Dennis W. & Kipnis Victor & Freedman Laurence S. & Dodd Kevin W. & Carroll Raymond J, 2011. "Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-32, January.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:1
    DOI: 10.2202/1557-4679.1267
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1267
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1267?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. Davidson, Russell & MacKinnon, James G, 1999. "Bootstrap Testing in Nonlinear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 487-508, May.
    3. Samiran Sinha & Bani K. Mallick & Victor Kipnis & Raymond J. Carroll, 2010. "Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error," Biometrics, The International Biometric Society, vol. 66(2), pages 444-454, June.
    4. Staudenmayer, John & Ruppert, David & Buonaccorsi, John P., 2008. "Density Estimation in the Presence of Heteroscedastic Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 726-736, June.
    5. Leung, Siu Fai & Yu, Shihti, 1996. "On the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 197-229.
    6. Victor Kipnis & Douglas Midthune & Dennis W. Buckman & Kevin W. Dodd & Patricia M. Guenther & Susan M. Krebs-Smith & Amy F. Subar & Janet A. Tooze & Raymond J. Carroll & Laurence S. Freedman, 2009. "Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes," Biometrics, The International Biometric Society, vol. 65(4), pages 1003-1010, December.
    7. Johnson, Brent A. & Herring, Amy H. & Ibrahim, Joseph G. & Siega-Riz, Anna Maria, 2007. "Structured Measurement Error in Nutritional Epidemiology: Applications in the Pregnancy, Infection, and Nutrition (PIN) Study," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 856-866, September.
    8. Ekaterini Kyriazidou, 1997. "Estimation of a Panel Data Sample Selection Model," Econometrica, Econometric Society, vol. 65(6), pages 1335-1364, November.
    9. Liang Li & Jun Shao & Mari Palta, 2005. "A Longitudinal Measurement Error Model with a Semicontinuous Covariate," Biometrics, The International Biometric Society, vol. 61(3), pages 824-830, September.
    10. Wand, M. P., 1998. "Finite sample performance of deconvolving density estimators," Statistics & Probability Letters, Elsevier, vol. 37(2), pages 131-139, February.
    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. repec:jss:jstsof:46:c03 is not listed on IDEAS

    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. Cairns, Alexander P. & Meilke, Karl D., 2012. "Canadian Agrifood Export Performance and the Growth Potential of the BRICs and Next-11," Trade Policy Briefs 145973, Canadian Agricultural Trade Policy Research Network.
    2. Takashi Yamagata & Chris Orme, 2005. "On Testing Sample Selection Bias Under the Multicollinearity Problem," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 467-481.
    3. Francis Teal & Måns Söderbom, 2002. "Does firm size really affect earnings?," Economics Series Working Papers WPS/2002-08, University of Oxford, Department of Economics.
    4. Saulo, Helton & Vila, Roberto & Cordeiro, Shayane S. & Leiva, Víctor, 2023. "Bivariate symmetric Heckman models and their characterization," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    5. Goic, Marcel & Rojas, Andrea & Saavedra, Ignacio, 2021. "The Effectiveness of Triggered Email Marketing in Addressing Browse Abandonments," Journal of Interactive Marketing, Elsevier, vol. 55(C), pages 118-145.
    6. Marjan Petreski & Nikica Blazevski & Blagica Petreski, 2014. "Gender Wage Gap when Women are Highly Inactive: Evidence from Repeated Imputations with Macedonian Data," Journal of Labor Research, Springer, vol. 35(4), pages 393-411, December.
    7. Lewbel, Arthur, 2007. "Endogenous selection or treatment model estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 777-806, December.
    8. Kyung‐Rae Hyun & Sungwook Kang & Sunmi Lee, 2016. "Population Aging and Healthcare Expenditure in Korea," Health Economics, John Wiley & Sons, Ltd., vol. 25(10), pages 1239-1251, October.
    9. S. Trevis Certo & John R. Busenbark & Hyun‐soo Woo & Matthew Semadeni, 2016. "Sample selection bias and Heckman models in strategic management research," Strategic Management Journal, Wiley Blackwell, vol. 37(13), pages 2639-2657, December.
    10. Kalwij, Adriaan S., 2003. "A maximum likelihood estimator based on first differences for a panel data Tobit model with individual specific effects," Economics Letters, Elsevier, vol. 81(2), pages 165-172, November.
    11. Helton Saulo & Roberto Vila & Shayane S. Cordeiro, 2022. "Symmetric generalized Heckman models," Papers 2206.10054, arXiv.org.
    12. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    13. Yousri Slaoui, 2021. "Data-driven Deconvolution Recursive Kernel Density Estimators Defined by Stochastic Approximation Method," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 312-352, February.
    14. Chirok Han & Goeun Lee, 2017. "Efficient Estimation of Linear Panel Data Models with Sample Selection and Fixed Effects," Discussion Paper Series 1707, Institute of Economic Research, Korea University.
    15. Seonho Shin, 2022. "To work or not? Wages or subsidies?: Copula-based evidence of subsidized refugees’ negative selection into employment," Empirical Economics, Springer, vol. 63(4), pages 2209-2252, October.
    16. Buligescu, B. & de Crombrugghe, D.P.I. & Mentesoglu, G. & Montizaan, R.M., 2008. "Estimating the wage penalty for maternal leave," ROA Research Memorandum 005, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    17. repec:jss:jstsof:27:i07 is not listed on IDEAS
    18. Madden, David, 2008. "Sample selection versus two-part models revisited: The case of female smoking and drinking," Journal of Health Economics, Elsevier, vol. 27(2), pages 300-307, March.
    19. Fernández-Val, Iván & Vella, Francis, 2011. "Bias corrections for two-step fixed effects panel data estimators," Journal of Econometrics, Elsevier, vol. 163(2), pages 144-162, August.
    20. Eun-Ju Lee & David Eastwood & Jinkook Lee, 2004. "A Sample Selection Model of Consumer Adoption of Computer Banking," Journal of Financial Services Research, Springer;Western Finance Association, vol. 26(3), pages 263-275, December.
    21. Bo E. Honore & Luojia Hu, 2021. "Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification," Working Paper Series WP 2022-33, Federal Reserve Bank of Chicago.

    More about this item

    Statistics

    Access and download statistics

    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:bpj:ijbist:v:7:y:2011:i:1:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.