Asymptotically Optimal Regression Prediction Intervals and Prediction Regions for Multivariate Data

David Olive

Abstract


This paper presents asymptotically optimal prediction intervals and prediction regions. The prediction intervals are for a future response $Y_f$ given a $p \times 1$ vector $\bx_f$ of predictors when the regression model has the form $Y_i = m(\bx_i) + e_i$ where $m$ is a function of $\bx_i$ and the errors $e_i$ are iid from a continuous unimodal distribution. The prediction intervals have coverage near or higher than the nominal coverage for many techniques even for moderate sample size $n$, say $n >$ 10(model degrees of freedom). The prediction regions are for a future vector of measurements $\bx_f$ from a multivariate distribution. The nonparametric prediction region developed in this paper has correct asymptotic coverage if the data $\bx_1, ..., \bx_n$ are iid from a distribution with a nonsingular covariance matrix. For many distributions, this prediction region appears to have good coverage for $n > 20 p$, and this region is asymptotically optimal on a large class of elliptically contoured distributions. Hence the prediction intervals and regions perform well for moderate sample sizes as well as asymptotically.

Full Text: PDF DOI: 10.5539/ijsp.v2n1p90

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International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

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