Models as Approximations I: Consequences Illustrated with Linear Regression

Article Properties
  • DOI (url)
    10.1214/18-sts693
  • Publication Date
    2019/11/01
  • Journal
    Statistical Science
  • Indian UGC (journal)
  • Refrences
    60
  • Andreas Buja
  • Lawrence Brown
  • Richard Berk
  • Edward George
  • Emil Pitkin
  • Mikhail Traskin
  • Kai Zhang
  • Linda Zhao
Cite
Buja, Andreas, et al. “Models As Approximations I: Consequences Illustrated With Linear Regression”. Statistical Science, vol. 34, no. 4, 2019, https://doi.org/10.1214/18-sts693.
Buja, A., Brown, L., Berk, R., George, E., Pitkin, E., Traskin, M., Zhang, K., & Zhao, L. (2019). Models as Approximations I: Consequences Illustrated with Linear Regression. Statistical Science, 34(4). https://doi.org/10.1214/18-sts693
Buja, Andreas, Lawrence Brown, Richard Berk, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang, and Linda Zhao. “Models As Approximations I: Consequences Illustrated With Linear Regression”. Statistical Science 34, no. 4 (2019). https://doi.org/10.1214/18-sts693.
Buja A, Brown L, Berk R, George E, Pitkin E, Traskin M, et al. Models as Approximations I: Consequences Illustrated with Linear Regression. Statistical Science. 2019;34(4).
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
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Refrences
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 3,004 1982
Consequences and Detection of Misspecified Nonlinear Regression Models Journal of the American Statistical Association
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 133 1981
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity Econometrica
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Using Least Squares to Approximate Unknown Regression Functions International Economic Review
  • Social Sciences: Commerce: Business
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  • Social Sciences: Economic theory. Demography: Economics as a science
 258 1980