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Haiying Wang - One of the best experts on this subject based on the ideXlab platform.
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Model averaging for varying coefficient partially linear Measurement Error Models
Electronic Journal of Statistics, 2012Co-Authors: Haiying Wang, Guohua Zou, Alan T K WanAbstract:In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of Model average estimators under parametric Models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by Model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varying-coefficient partially linear Measurement Error Model. Within this context, we develop a Model averaging scheme for the unknowns, derive the Model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the Model average estimators are asymptotically the same as those obtained under the full Model. A simulation study examines the finite sample performance of the Model average estimators, and a real data analysis illustrates the application of the method in practice.
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Model averaging for varying coefficient partially linear Measurement Error Models
Electronic Journal of Statistics, 2012Co-Authors: Haiying Wang, Guohua Zou, Alan T K WanAbstract:In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of Model average estimators under parametric Models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by Model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varyingcoefficient partially linear Measurement Error Model. Within this context, we develop a Model averaging scheme for the unknowns, derive the Model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the Model average estimators are asymptotically the same as those obtained under the full Model. A simulation study examines the finite sample performance of the Model average estimators, and a real data analysis illustrates the application of the method in practice. AMS 2000 subject classifications: Primary 62E20; secondary 62F10, 62F12.
Victor Kipnis - One of the best experts on this subject based on the ideXlab platform.
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a new multivariate Measurement Error Model with zero inflated dietary data and its application to dietary assessment
The Annals of Applied Statistics, 2011Co-Authors: Saijuan Zhang, Victor Kipnis, Douglas Midthune, Patricia M Guenther, Susan M Krebssmith, Kevin W Dodd, Dennis W Buckman, Janet A Tooze, Laurence S Freedman, Raymond J. CarrollAbstract:The methodology is illustrated through an application to estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.
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a new multivariate Measurement Error Model with zero inflated dietary data and its application to dietary assessment
The Annals of Applied Statistics, 2011Co-Authors: Saijuan Zhang, Victor Kipnis, Douglas Midthune, Patricia M Guenther, Susan M Krebssmith, Kevin W Dodd, Dennis W Buckman, Janet A Tooze, Laurence S Freedman, Raymond J. CarrollAbstract:The methodology is illustrated through an application to estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.
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performance of a food frequency questionnaire in the us nih aarp national institutes of health american association of retired persons diet and health study
Public Health Nutrition, 2008Co-Authors: Frances E Thompson, Raymond J. Carroll, Amy F Subar, Victor Kipnis, Douglas Midthune, Laurence S Freedman, Charles C Brown, Matthew S Butcher, Traci Mouw, Michael F LeitzmannAbstract:Objective We evaluated the performance of the food-frequency questionnaire (FFQ) administered to participants in the US NIH–AARP (National Institutes of Health–American Association of Retired Persons) Diet and Health Study, a cohort of 566 404 persons living in the USA and aged 50–71 years at baseline in 1995. Design The 124-item FFQ was evaluated within a Measurement Error Model using two non-consecutive 24-hour dietary recalls (24HRs) as the reference. Setting Participants were from six states (California, Florida, Pennsylvania, New Jersey, North Carolina and Louisiana) and two metropolitan areas (Atlanta, Georgia and Detroit, Michigan). Subjects A subgroup of the cohort consisting of 2053 individuals. Results For the 26 nutrient constituents examined, estimated correlations with true intake (not energy-adjusted) ranged from 0.22 to 0.67, and attenuation factors ranged from 0.15 to 0.49. When adjusted for reported energy intake, performance improved; estimated correlations with true intake ranged from 0.36 to 0.76, and attenuation factors ranged from 0.24 to 0.68. These results compare favourably with those from other large prospective studies. However, previous biomarker-based studies suggest that, due to correlation of Errors in FFQs and self-report reference instruments such as the 24HR, the correlations and attenuation factors observed in most calibration studies, including ours, tend to overestimate FFQ performance. Conclusion The performance of the FFQ in the NIH–AARP Diet and Health Study, in conjunction with the study’s large sample size and wide range of dietary intake, is likely to allow detection of moderate (≥1.8) relative risks between many energy-adjusted nutrients and common cancers.
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fruit and vegetable assessment performance of 2 new short instruments and a food frequency questionnaire
Journal of The American Dietetic Association, 2002Co-Authors: Frances E Thompson, Amy F Subar, Douglas Midthune, Albert F Smith, Kathy L Radimer, Lisa Kahle, Victor KipnisAbstract:Abstract Objective To evaluate the ability of 2 new short assessment instruments and a food frequency questionnaire (FFQ) to measure intake of fruit and vegetables. The "All-Day" screener asks frequency and portion size questions about 9 food items. The "By-Meal" screener is similar, except that it asks about 2 of those 9 food items in terms of mealtime. Design Survey participants completed 4 telephone-administered 24-hour dietary recalls over 1 year, a self-administered FFQ 1 to 2 months later, and 1 of 2 self-administered screeners after an additional 7 months. Subjects/setting Participating were 202 men and 260 women aged 20 to 70 years living throughout the United States. Statistical analyses Fruit and vegetable intakes measured by each screener and the FFQ were compared with true usual intake based on a Measurement Error Model with 24-hour dietary recalls as the reference instrument. Results Estimates of median daily servings of fruit and vegetables were as follows: For men: True intake (5.8) vs All-Day screener (5.0), By-Meal screener (5.5), and FFQ (6.6); for women: true intake (4.2) vs All-Day screener (5.0), By-Meal screener (5.4), and FFQ (6.2). Estimated correlations between the test instruments and true intake were as follows: For men: All-Day screener (0.66), By-Meal screener (0.67), FFQ (0.68); for women: All-Day screener (0.51), By-Meal screener (0.53), and FFQ (0.54). Applications/conclusions Both screeners might be useful to estimate median intakes of fruit and vegetable servings in US populations, but they might be less useful in accurately ranking individuals. More research is needed before using the screeners in ethnic or low-literacy populations. J Am Diet Assoc. 2002;102:1764-1772 .
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comparative validation of the block willett and national cancer institute food frequency questionnaires the eating at america s table study
American Journal of Epidemiology, 2001Co-Authors: Amy F Subar, Frances E Thompson, Suzanne Mcnutt, Victor Kipnis, Douglas Midthune, Paul Hurwitz, Anna Mcintosh, Simon RosenfeldAbstract:Researchers at the National Cancer Institute developed a new cognitively based food frequency questionnaire (FFQ), the Diet History Questionnaire (DHQ). The Eating at America's Table Study sought to validate and compare the DHQ with the Block and Willett FFQs. Of 1,640 men and women recruited to participate from a nationally representative sample in 1997, 1,301 completed four telephone 24-hour recalls, one in each season. Participants were randomized to receive either a DHQ and Block FFQ or a DHQ and Willett FFQ. With a standard Measurement Error Model, correlations for energy between estimated truth and the DHQ, Block FFQ, and Willett FFQ, respectively, were 0.48, 0.45, and 0.18 for women and 0.49, 0.45, and 0.21 for men. For 26 nutrients, correlations and attenuation coefficients were somewhat higher for the DHQ versus the Block FFQ, and both were better than the Willett FFQ in Models unadjusted for energy. Energy adjustment increased correlations and attenuation coefficients for the Willett FFQ dramatically and for the DHQ and Block FFQ instruments modestly. The DHQ performed best overall. These data show that the DHQ and the Block FFQ are better at estimating absolute intakes than is the Willett FFQ but that, after energy adjustment, all three are more comparable for purposes of assessing diet-disease risk.
Raymond J. Carroll - One of the best experts on this subject based on the ideXlab platform.
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moment reconstruction and moment adjusted imputation when exposure is generated by a complex nonlinear random effects Modeling process
Biometrics, 2016Co-Authors: Cornelis J Potgieter, Raymond J. Carroll, Victor Kipnis, Laurence S FreedmanAbstract:Summary For the classical, homoscedastic Measurement Error Model, moment reconstruction (Freedman et al., 2004, 2008) and moment-adjusted imputation (Thomas et al., 2011) are appealing, computationally simple imputation-like methods for general Model fitting. Like classical regression calibration, the idea is to replace the unobserved variable subject to Measurement Error with a proxy that can be used in a variety of analyses. Moment reconstruction and moment-adjusted imputation differ from regression calibration in that they attempt to match multiple features of the latent variable, and also to match some of the latent variable's relationships with the response and additional covariates. In this note, we consider a problem where true exposure is generated by a complex, nonlinear random effects Modeling process, and develop analogues of moment reconstruction and moment-adjusted imputation for this case. This general Model includes classical Measurement Errors, Berkson Measurement Errors, mixtures of Berkson and classical Errors and problems that are not Measurement Error problems, but also cases where the data-generating process for true exposure is a complex, nonlinear random effects Modeling process. The methods are illustrated using the National Institutes of Health–AARP Diet and Health Study where the latent variable is a dietary pattern score called the Healthy Eating Index-2005. We also show how our general Model includes methods used in radiation epidemiology as a special case. Simulations are used to illustrate the methods.
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a new multivariate Measurement Error Model with zero inflated dietary data and its application to dietary assessment
The Annals of Applied Statistics, 2011Co-Authors: Saijuan Zhang, Victor Kipnis, Douglas Midthune, Patricia M Guenther, Susan M Krebssmith, Kevin W Dodd, Dennis W Buckman, Janet A Tooze, Laurence S Freedman, Raymond J. CarrollAbstract:The methodology is illustrated through an application to estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.
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a new multivariate Measurement Error Model with zero inflated dietary data and its application to dietary assessment
The Annals of Applied Statistics, 2011Co-Authors: Saijuan Zhang, Victor Kipnis, Douglas Midthune, Patricia M Guenther, Susan M Krebssmith, Kevin W Dodd, Dennis W Buckman, Janet A Tooze, Laurence S Freedman, Raymond J. CarrollAbstract:The methodology is illustrated through an application to estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.
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performance of a food frequency questionnaire in the us nih aarp national institutes of health american association of retired persons diet and health study
Public Health Nutrition, 2008Co-Authors: Frances E Thompson, Raymond J. Carroll, Amy F Subar, Victor Kipnis, Douglas Midthune, Laurence S Freedman, Charles C Brown, Matthew S Butcher, Traci Mouw, Michael F LeitzmannAbstract:Objective We evaluated the performance of the food-frequency questionnaire (FFQ) administered to participants in the US NIH–AARP (National Institutes of Health–American Association of Retired Persons) Diet and Health Study, a cohort of 566 404 persons living in the USA and aged 50–71 years at baseline in 1995. Design The 124-item FFQ was evaluated within a Measurement Error Model using two non-consecutive 24-hour dietary recalls (24HRs) as the reference. Setting Participants were from six states (California, Florida, Pennsylvania, New Jersey, North Carolina and Louisiana) and two metropolitan areas (Atlanta, Georgia and Detroit, Michigan). Subjects A subgroup of the cohort consisting of 2053 individuals. Results For the 26 nutrient constituents examined, estimated correlations with true intake (not energy-adjusted) ranged from 0.22 to 0.67, and attenuation factors ranged from 0.15 to 0.49. When adjusted for reported energy intake, performance improved; estimated correlations with true intake ranged from 0.36 to 0.76, and attenuation factors ranged from 0.24 to 0.68. These results compare favourably with those from other large prospective studies. However, previous biomarker-based studies suggest that, due to correlation of Errors in FFQs and self-report reference instruments such as the 24HR, the correlations and attenuation factors observed in most calibration studies, including ours, tend to overestimate FFQ performance. Conclusion The performance of the FFQ in the NIH–AARP Diet and Health Study, in conjunction with the study’s large sample size and wide range of dietary intake, is likely to allow detection of moderate (≥1.8) relative risks between many energy-adjusted nutrients and common cancers.
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Measurement Error in nonlinear Models a modern perspective
2006Co-Authors: Raymond J. CarrollAbstract:Guide to Notation Introduction The Double/Triple-Whammy of Measurement Error Classical Measurement Error A Nutrition Example Measurement Error Examples Radiation Epidemiology and Berkson Errors Classical Measurement Error Model Extensions Other Examples of Measurement Error Models Checking The Classical Error Model Loss of Power A Brief Tour Bibliographic Notes Important Concepts Functional and Structural Models Models for Measurement Error Sources of Data Is There an "Exact" Predictor? What is Truth? Differential and Nondifferential Error Prediction Bibliographic Notes Linear Regression and Attenuation Introduction Bias Caused by Measurement Error Multiple and Orthogonal Regression Correcting for Bias Bias Versus Variance Attenuation in General Problems Bibliographic Notes Regression Calibration Overview The Regression Calibration Algorithm NHANES Example Estimating the Calibration Function Parameters Multiplicative Measurement Error Standard Errors Expanded Regression Calibration Models Examples of the Approximations Theoretical Examples Bibliographic Notes and Software Simulation Extrapolation Overview Simulation Extrapolation Heuristics The SIMEX Algorithm Applications SIMEX in Some Important Special Cases Extensions and Related Methods Bibliographic Notes Instrumental Variables Overview Instrumental Variables in Linear Models Approximate Instrumental Variable Estimation Adjusted Score Method Examples Other Methodologies Bibliographic Notes Score Function Methods Overview Linear and Logistic Regression Conditional Score Functions Corrected Score Functions Computation and Asymptotic Approximations Comparison of Conditional and Corrected Scores Bibliographic Notes Likelihood and Quasilikelihood Introduction Steps 2 and 3: Constructing Likelihoods Step 4: Numerical Computation of Likelihoods Cervical Cancer and Herpes Framingham Data Nevada Test Site Reanalysis Bronchitis Example Quasilikelihood and Variance Function Models Bibliographic Notes Bayesian Methods Overview The Gibbs Sampler Metropolis-Hastings Algorithm Linear Regression Nonlinear Models Logistic Regression Berkson Errors Automatic implementation Cervical Cancer and Herpes Framingham Data OPEN Data: A Variance Components Model Bibliographic Notes Hypothesis Testing Overview The Regression Calibration Approximation Illustration: OPEN Data Hypotheses about Sub-Vectors of ssx and ssz Efficient Score Tests of H0 : ssx = 0 Bibliographic Notes Longitudinal Data and Mixed Models Mixed Models for Longitudinal Data Mixed Measurement Error Models A Bias Corrected Estimator SIMEX for GLMMEMs Regression Calibration for GLMMs Maximum Likelihood Estimation Joint Modeling Other Models and Applications Example: The CHOICE Study Bibliographic Notes Nonparametric Estimation Deconvolution Nonparametric Regression Baseline Change Example Bibliographic Notes Semiparametric Regression Overview Additive Models MCMC for Additive Spline Models Monte-Carlo EM-Algorithm Simulation with Classical Errors Simulation with Berkson Errors Semiparametrics: X Modeled Parametrically Parametric Models: No Assumptions on X Bibliographic Notes Survival Data Notation and Assumptions Induced Hazard Function Regression Calibration for Survival Analysis SIMEX for Survival Analysis Chronic Kidney Disease Progression Semi and Nonparametric Methods Likelihood Inference for Frailty Models Bibliographic Notes Response Variable Error Response Error and Linear Regression Other Forms of Additive Response Error Logistic Regression with Response Error Likelihood Methods Use of Complete Data Only Semiparametric Methods for Validation Data Bibliographic Notes Appendix A: Background Material Overview Normal and Lognormal Distributions Gamma and Inverse Gamma Distributions Best and Best Linear Prediction and Regression Likelihood Methods Unbiased Estimating Equations Quasilikelihood and Variance Function Models (QVF) Generalized Linear Models Bootstrap Methods Appendix B: Technical Details Appendix to Chapter 1: Power in Berkson and Classical Error Models Appendix to Chapter 3: Linear Regression and Attenuation Regression Calibration SIMEX Instrumental Variables Score Function Methods Likelihood and Quasilikelihood Bayesian Methods References Applications and Examples Index Index
Alan T K Wan - One of the best experts on this subject based on the ideXlab platform.
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Model averaging for varying coefficient partially linear Measurement Error Models
Electronic Journal of Statistics, 2012Co-Authors: Haiying Wang, Guohua Zou, Alan T K WanAbstract:In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of Model average estimators under parametric Models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by Model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varying-coefficient partially linear Measurement Error Model. Within this context, we develop a Model averaging scheme for the unknowns, derive the Model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the Model average estimators are asymptotically the same as those obtained under the full Model. A simulation study examines the finite sample performance of the Model average estimators, and a real data analysis illustrates the application of the method in practice.
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Model averaging for varying coefficient partially linear Measurement Error Models
Electronic Journal of Statistics, 2012Co-Authors: Haiying Wang, Guohua Zou, Alan T K WanAbstract:In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of Model average estimators under parametric Models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by Model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varyingcoefficient partially linear Measurement Error Model. Within this context, we develop a Model averaging scheme for the unknowns, derive the Model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the Model average estimators are asymptotically the same as those obtained under the full Model. A simulation study examines the finite sample performance of the Model average estimators, and a real data analysis illustrates the application of the method in practice. AMS 2000 subject classifications: Primary 62E20; secondary 62F10, 62F12.
Alexander Kukush - One of the best experts on this subject based on the ideXlab platform.
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asymptotic normality of total least squares estimator in a multivariate Errors in variables Model ax b
arXiv: Probability, 2016Co-Authors: Alexander Kukush, Yaroslav TsaregorodtsevAbstract:We consider a multivariate functional Measurement Error Model $AX\approx B$. The Errors in $[A,B]$ are uncorrelated, row-wise independent, and have equal (unknown) variances. We study the total least squares estimator of $X$, which, in the case of normal Errors, coincides with the maximum likelihood one. We give conditions for asymptotic normality of the estimator when the number of rows in $A$ is increasing. Under mild assumptions, the covariance structure of the limit Gaussian random matrix is nonsingular. For normal Errors, the results can be used to construct an asymptotic confidence interval for a linear functional of $X$.
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estimation in a linear multivariate Measurement Error Model with a change point in the data
Computational Statistics & Data Analysis, 2007Co-Authors: Alexander Kukush, Ivan Markovsky, S Van HuffelAbstract:A linear multivariate Measurement Error Model AX=B is considered. The Errors in [AB] are row-wise finite dependent, and within each row, the Errors may be correlated. Some of the columns may be observed without Errors, and in addition the Error covariance matrix may differ from row to row. The columns of the Error matrix are united into two uncorrelated blocks, and in each block, the total covariance structure is supposed to be known up to a corresponding scalar factor. Moreover the row data are clustered into two groups, according to the behavior of the rows of true A matrix. The change point is unknown and estimated in the paper. After that, based on the method of corrected objective function, strongly consistent estimators of the scalar factors and X are constructed, as the numbers of rows in the clusters tend to infinity. Since Toeplitz/Hankel structure is allowed, the results are applicable to system identification, with a change point in the input data.
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consistent estimation in an implicit quadratic Measurement Error Model
Computational Statistics & Data Analysis, 2004Co-Authors: Alexander Kukush, Ivan Markovsky, Sabine Van HuffelAbstract:An adjusted least squares estimator is derived that yields a consistent estimate of the parameters of an implicit quadratic Measurement Error Model. In addition, a consistent estimator for the Measurement Error noise variance is proposed. Important assumptions are: (1) all Errors are uncorrelated identically distributed and (2) the Error distribution is normal. The estimators for the quadratic Measurement Error Model are used to estimate consistently conic sections and ellipsoids. Simulation examples, comparing the adjusted least squares estimator with the ordinary least squares method and the orthogonal regression method, are shown for the ellipsoid fitting problem.
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consistent fundamental matrix estimation in a quadratic Measurement Error Model arising in motion analysis
Computational Statistics & Data Analysis, 2002Co-Authors: Alexander Kukush, Ivan Markovsky, S Van HuffelAbstract:Consistent estimators of the rank-deficient fundamental matrix yielding information on the relative orientation of two images in two-view motion analysis are derived. The estimators are derived by minimizing a corrected contrast function in a quadratic Measurement Error Model. In addition, a consistent estimator for the Measurement Error variance is obtained. Simulation results show the improved accuracy of the newly proposed estimator compared to the ordinary total least-squares estimator.