Prediction and calibration for multiple correlated variables

Dulal K. Bhaumik and Rachel K. Nordgren

Journal of Multivariate Analysis, 2019, vol. 173, issue C, 313-327

Abstract: The standard approach for prediction of multiple correlated outcome measures overpredicts the unknown observation in the linear model setup if associated covariate measures follow a certain distribution. It is desired to have a nonempty confidence region when some covariate measures are missing and required to be estimated. This article develops a methodology for prediction and proposes a shrinkage predictor with a smaller risk compared to the one based on the maximum likelihood estimate. It also provides an algorithm for constructing a nonempty confidence region for unknown covariates. Proposed methodology is shown to perform well in terms of maintaining a smaller risk in prediction and the coverage probability in calibration. Results are illustrated with a recent behavioral science dataset.

Keywords: Confidence region; Multivariate analysis; Shrinkage (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.jmva.2019.03.001

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