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Statistical Analysis of Measurement Error Models and Applications
 
Front Cover for Statistical Analysis of Measurement Error Models and Applications
Available Formats:
Electronic ISBN: 978-0-8218-7700-5
Product Code: CONM/112.E
List Price: $71.00
MAA Member Price: $63.90
AMS Member Price: $56.80
Front Cover for Statistical Analysis of Measurement Error Models and Applications
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  • Front Cover for Statistical Analysis of Measurement Error Models and Applications
  • Back Cover for Statistical Analysis of Measurement Error Models and Applications
Statistical Analysis of Measurement Error Models and Applications
Available Formats:
Electronic ISBN:  978-0-8218-7700-5
Product Code:  CONM/112.E
List Price: $71.00
MAA Member Price: $63.90
AMS Member Price: $56.80
  • Book Details
     
     
    Contemporary Mathematics
    Volume: 1121990; 248 pp
    MSC: Primary 62;

    Measurement error models describe functional relationships among variables observed, subject to random errors of measurement. Examples include linear and nonlinear errors-in-variables regression models, calibration and inverse regression models, factor analysis models, latent structure models, and simultaneous equations models. Such models are used in a wide variety of areas, including medicine, the life sciences, econometrics, chemometrics, geology, sample surveys, and time series. Although the problem of estimating the parameters of such models exists in most scientific fields, there is a need for more sources that treat measurement error models as an area of statistical methodology. This volume is designed to address that need.

    This book contains the proceedings of an AMS-IMS-SIAM Joint Summer Research Conference in the Mathematical Sciences on Statistical Analysis of Measurement Error Models and Applications. The conference was held at Humboldt State University in Arcata, California in June 1989. The papers in this volume fall into four broad groups. The first group treats general aspects of the measurement problem and features a discussion of the history of measurement error models. The second group focuses on inference for the nonlinear measurement error model, an active area of research which generated considerable interest at the conference. The third group of papers examines computational aspects of estimation, while the final set studies estimators possessing robustness properties against deviations from common model assumptions.

  • Table of Contents
     
     
    • General Problems [ MR 1087094 ]
    • Peter Sprent - Some history of functional and structural relationships [ MR 1087095 ]
    • Alice S. Whittemore - Errors-in-variables regression problems in epidemiology [ MR 1087096 ]
    • Hans Schneeweiss - Models with latent variables: LISREL versus PLS [ MR 1087097 ]
    • Wayne A. Fuller - Prediction of true values for the measurement error model [ MR 1087098 ]
    • Stephen M. Miller - Analysis of residuals from measurement error models [ MR 1087099 ]
    • John L. Eltinge - Errors-in-variables estimation in the presence of serially correlated observations [ MR 1087100 ]
    • Nonlinear Models [ MR 1087094 ]
    • Leon Jay Gleser - Improvements of the naive approach to estimation in nonlinear errors-in-variables regression models [ MR 1087101 ]
    • Leonard A. Stefanski and Raymond J. Carroll - Structural logistic regression measurement error models [ MR 1087102 ]
    • Daniel W. Schafer - Measurement error model estimation using iteratively weighted least squares [ MR 1087103 ]
    • Philip J. Brown and Samuel D. Oman - Problematic points in nonlinear calibration [ MR 1087104 ]
    • Yasuo Amemiya - Instrumental variable estimation of the nonlinear measurement error model [ MR 1087105 ]
    • Daniel J. Schnell - A likelihood ratio test for error covariance specification in nonlinear measurement error models [ MR 1087106 ]
    • Cliff H. Spiegelman - Plotting techniques for errors-in-variables problems [ MR 1087107 ]
    • Computational Aspects [ MR 1087094 ]
    • G. W. Stewart - Perturbation theory and least squares with errors in the variables [ MR 1087108 ]
    • Paul T. Boggs and Janet E. Rogers - Orthogonal distance regression [ MR 1087109 ]
    • Nicholas J. Higham - Computing error bounds for regression problems [ MR 1087110 ]
    • Robust Procedures [ MR 1087094 ]
    • M. W. Browne - Asymptotic robustness of normal theory methods for the analysis of latent curves [ MR 1087111 ]
    • Chi Lun Cheng and John W. Van Ness - Bounded influence errors-in-variables regression [ MR 1087112 ]
    • Víctor J. Yohai and Ruben H. Zamar - Bounded influence estimation in the errors-in-variables model [ MR 1087113 ]
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Volume: 1121990; 248 pp
MSC: Primary 62;

Measurement error models describe functional relationships among variables observed, subject to random errors of measurement. Examples include linear and nonlinear errors-in-variables regression models, calibration and inverse regression models, factor analysis models, latent structure models, and simultaneous equations models. Such models are used in a wide variety of areas, including medicine, the life sciences, econometrics, chemometrics, geology, sample surveys, and time series. Although the problem of estimating the parameters of such models exists in most scientific fields, there is a need for more sources that treat measurement error models as an area of statistical methodology. This volume is designed to address that need.

This book contains the proceedings of an AMS-IMS-SIAM Joint Summer Research Conference in the Mathematical Sciences on Statistical Analysis of Measurement Error Models and Applications. The conference was held at Humboldt State University in Arcata, California in June 1989. The papers in this volume fall into four broad groups. The first group treats general aspects of the measurement problem and features a discussion of the history of measurement error models. The second group focuses on inference for the nonlinear measurement error model, an active area of research which generated considerable interest at the conference. The third group of papers examines computational aspects of estimation, while the final set studies estimators possessing robustness properties against deviations from common model assumptions.

  • General Problems [ MR 1087094 ]
  • Peter Sprent - Some history of functional and structural relationships [ MR 1087095 ]
  • Alice S. Whittemore - Errors-in-variables regression problems in epidemiology [ MR 1087096 ]
  • Hans Schneeweiss - Models with latent variables: LISREL versus PLS [ MR 1087097 ]
  • Wayne A. Fuller - Prediction of true values for the measurement error model [ MR 1087098 ]
  • Stephen M. Miller - Analysis of residuals from measurement error models [ MR 1087099 ]
  • John L. Eltinge - Errors-in-variables estimation in the presence of serially correlated observations [ MR 1087100 ]
  • Nonlinear Models [ MR 1087094 ]
  • Leon Jay Gleser - Improvements of the naive approach to estimation in nonlinear errors-in-variables regression models [ MR 1087101 ]
  • Leonard A. Stefanski and Raymond J. Carroll - Structural logistic regression measurement error models [ MR 1087102 ]
  • Daniel W. Schafer - Measurement error model estimation using iteratively weighted least squares [ MR 1087103 ]
  • Philip J. Brown and Samuel D. Oman - Problematic points in nonlinear calibration [ MR 1087104 ]
  • Yasuo Amemiya - Instrumental variable estimation of the nonlinear measurement error model [ MR 1087105 ]
  • Daniel J. Schnell - A likelihood ratio test for error covariance specification in nonlinear measurement error models [ MR 1087106 ]
  • Cliff H. Spiegelman - Plotting techniques for errors-in-variables problems [ MR 1087107 ]
  • Computational Aspects [ MR 1087094 ]
  • G. W. Stewart - Perturbation theory and least squares with errors in the variables [ MR 1087108 ]
  • Paul T. Boggs and Janet E. Rogers - Orthogonal distance regression [ MR 1087109 ]
  • Nicholas J. Higham - Computing error bounds for regression problems [ MR 1087110 ]
  • Robust Procedures [ MR 1087094 ]
  • M. W. Browne - Asymptotic robustness of normal theory methods for the analysis of latent curves [ MR 1087111 ]
  • Chi Lun Cheng and John W. Van Ness - Bounded influence errors-in-variables regression [ MR 1087112 ]
  • Víctor J. Yohai and Ruben H. Zamar - Bounded influence estimation in the errors-in-variables model [ MR 1087113 ]
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