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Full-Text Articles in Biostatistics
Concentrations Of Criteria Pollutants In The Contiguous U.S., 1979 – 2015: Role Of Model Parsimony In Integrated Empirical Geographic Regression, Sun-Young Kim, Matthew Bechle, Steve Hankey, Elizabeth (Lianne) A. Sheppard, Adam A. Szpiro, Julian D. Marshall
Concentrations Of Criteria Pollutants In The Contiguous U.S., 1979 – 2015: Role Of Model Parsimony In Integrated Empirical Geographic Regression, Sun-Young Kim, Matthew Bechle, Steve Hankey, Elizabeth (Lianne) A. Sheppard, Adam A. Szpiro, Julian D. Marshall
UW Biostatistics Working Paper Series
BACKGROUND: National- or regional-scale prediction models that estimate individual-level air pollution concentrations commonly include hundreds of geographic variables. However, these many variables may not be necessary and parsimonious approach including small numbers of variables may achieve sufficient prediction ability. This parsimonious approach can also be applied to most criteria pollutants. This approach will be powerful when generating publicly available datasets of model predictions that support research in environmental health and other fields. OBJECTIVES: We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants, for all years with regulatory monitoring data …
A Spline-Assisted Semiparametric Approach To Nonparametric Measurement Error Models, Fei Jiang, Yanyuan Ma
A Spline-Assisted Semiparametric Approach To Nonparametric Measurement Error Models, Fei Jiang, Yanyuan Ma
COBRA Preprint Series
Nonparametric estimation of the probability density function of a random variable measured with error is considered to be a difficult problem, in the sense that depending on the measurement error prop- erty, the estimation rate can be as slow as the logarithm of the sample size. Likewise, nonparametric estimation of the regression function with errors in the covariate suffers the same possibly slow rate. The traditional methods for both problems are based on deconvolution, where the slow convergence rate is caused by the quick convergence to zero of the Fourier transform of the measurement error density, which, unfortunately, appears in …
Technical Considerations In The Use Of The E-Value, Tyler J. Vanderweele, Peng Ding, Maya Mathur
Technical Considerations In The Use Of The E-Value, Tyler J. Vanderweele, Peng Ding, Maya Mathur
Harvard University Biostatistics Working Paper Series
The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would have to have with both the exposure and the outcome, conditional on the measured covariates, to explain away the observed exposure-outcome association. We have elsewhere proposed that the reporting of E-values for estimates and for the limit of the confidence interval closest to the null become routine whenever causal effects are of interest. A number of questions have arisen about the use of E-value including questions concerning the interpretation of the relevant confounding association parameters, the nature of the transformation …