Statistical Analysis of Measurement Error Models and Applications: Proceedings of the AMS-IMS-SIAM Joint Summer Research Conference Held June 10-16, 1989, with Support from the National Science Foundation and the U.S. Army Research Office
Joint Summ Ams-Ims-Siam, Ams-Ims-Siam Joint Summer Research Conference in the Mathematical Scie, Philip J. Brown, Wayne A. Fuller
American Mathematical Soc., 1990 - 248 pagine
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.
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1990 American Mathematical algorithm Amenmiya Amer analysis approach approximation Assoc assumed assumptions backward error bias Biometrika Boggs bound cancer coefficients columns components computed conditional variance considered consistent estimator covariance matrix Covariate measurement error defined denote discrepancy function discussion double points Editors efficient equation error variances errors-in-variables model example factor Gaussian given Gleser independent instrumental variable estimator Iowa State University latent variables Lemma linear models LISREL logistic regression M-estimation Mathematics Subject Classification Mathematics Volume 112 maximum likelihood estimator mean square error measurement error models methods minimizes minimum discrepancy multivariate nonlinear nonlinear regression normally distributed observed obtained ODRPACK orthogonal regression parameter estimation perturbation positive definite prediction predictor problem procedure random variables regression model residuals robust row vector sample Schnabel Section sequence ſº solution Stefanski structural model test statistic Theorem theory tion true values W. A. Fuller zero