The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform
William C. Bridges - One of the best experts on this subject based on the ideXlab platform.
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The Impact of Correlated and/or Interacting Predictor Omission on Estimated Regression Coefficients in Linear Regression
Journal of Statistical Theory and Practice, 2019Co-Authors: Emily Nystrom, Julia L. Sharp, William C. BridgesAbstract:We examine cases of predictor omission defined by the relationship between the set of omitted predictor(s) and a set of remaining predictor(s), both of which are included in the full model. We consider a wider range of omitted predictors than previously studied by systematically accounting for both interaction and correlation between the included and the omitted predictors. Our study highlights the impact of predictor omission on the resulting Estimated Regression Coefficients and their squared standard errors. Theoretical and simulated results are presented to illustrate the impact of predictor omission among cases of interaction and correlation. In our simulated results, bias diverged as correlation increased from zero to one. On its own, interaction amplified bias, but the impact of interaction was worse when combined with correlation. Overall, our discussions surround the known problem of predictor omission with a rigorous framework to quantify bias in the included predictor’s Estimated Regression Coefficient and squared standard error.
Emily Nystrom - One of the best experts on this subject based on the ideXlab platform.
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The Impact of Correlated and/or Interacting Predictor Omission on Estimated Regression Coefficients in Linear Regression
Journal of Statistical Theory and Practice, 2019Co-Authors: Emily Nystrom, Julia L. Sharp, William C. BridgesAbstract:We examine cases of predictor omission defined by the relationship between the set of omitted predictor(s) and a set of remaining predictor(s), both of which are included in the full model. We consider a wider range of omitted predictors than previously studied by systematically accounting for both interaction and correlation between the included and the omitted predictors. Our study highlights the impact of predictor omission on the resulting Estimated Regression Coefficients and their squared standard errors. Theoretical and simulated results are presented to illustrate the impact of predictor omission among cases of interaction and correlation. In our simulated results, bias diverged as correlation increased from zero to one. On its own, interaction amplified bias, but the impact of interaction was worse when combined with correlation. Overall, our discussions surround the known problem of predictor omission with a rigorous framework to quantify bias in the included predictor’s Estimated Regression Coefficient and squared standard error.
Julia L. Sharp - One of the best experts on this subject based on the ideXlab platform.
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The Impact of Correlated and/or Interacting Predictor Omission on Estimated Regression Coefficients in Linear Regression
Journal of Statistical Theory and Practice, 2019Co-Authors: Emily Nystrom, Julia L. Sharp, William C. BridgesAbstract:We examine cases of predictor omission defined by the relationship between the set of omitted predictor(s) and a set of remaining predictor(s), both of which are included in the full model. We consider a wider range of omitted predictors than previously studied by systematically accounting for both interaction and correlation between the included and the omitted predictors. Our study highlights the impact of predictor omission on the resulting Estimated Regression Coefficients and their squared standard errors. Theoretical and simulated results are presented to illustrate the impact of predictor omission among cases of interaction and correlation. In our simulated results, bias diverged as correlation increased from zero to one. On its own, interaction amplified bias, but the impact of interaction was worse when combined with correlation. Overall, our discussions surround the known problem of predictor omission with a rigorous framework to quantify bias in the included predictor’s Estimated Regression Coefficient and squared standard error.
L I Linshan - One of the best experts on this subject based on the ideXlab platform.
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use cauchy schwarz inequality Estimated Regression Coefficient
Journal of Beijing Union University, 2008Co-Authors: L I LinshanAbstract:From the Cauchy-Schwarz inequality in form of random variable,two results of Second-order moment are obtained: Variances of random variable X、Y exists and not 0,if the probability of X、Y has linear relationship P{Y=aX+b}=1 is 1,then a=E(X-EX)(Y-EY)E(X-EX)~2,b=EY-aEX. Furthermore a method of estimating Regression Coefficient is also obtained.
D T Moore - One of the best experts on this subject based on the ideXlab platform.
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hazard rate ratio and prospective epidemiological studies
Journal of Clinical Epidemiology, 2002Co-Authors: M J Symons, D T MooreAbstract:Abstract Analysis of prospective follow-up data usually includes a Cox Regression model. When a hazard rate ratio, obtained as the exponential of an Estimated Regression Coefficient from the Cox model, is greater than 1.0, it consistently exceeds relative risk, and is exceeded by the odds ratio. The divergence of these distinct epidemiologic measures increases with the product of three factors: (1) the length of follow-up, (2) the average rate of the end point occurence over the follow-up period, and (3) the magnitude of risk, either above or below 1. Cornfield's rare disease assumption is basically the product of the first two of these factors. However, risks in excess of 2.5 have a powerful effect on the divergence of these measures, and this point has received less emphasis. Conversely, and as seen frequently in applications, relative risk, hazard rate ratio, and odds ratio numerically approximate one another with shorter follow-up, rarer end points, and risks closer to 1. Although the hazard rate ratio is not always distinguished from relative risk, it is commonly close to, and is always between, relative risk and the odds ratio. Consistent and accurate terminology would have us use hazard rate ratio with Cox Regression and odds ratio with logistic Regression. The term “relative risk” seems to be a default choice, regardless of the model being used. However, when relative risk is the object of the model chosen, as in a Poisson Regression approximation of two binomial proportions or an equivalent weighted least squares, then for us, relative risk is the accurate terminology.