|Title||Facilitating the Calculation of the Efficient Score Using Symbolic Computing.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Sibley, Alexander, Zhiguo Li, Yu Jiang, Yi-Ju Li, Cliburn Chan, Andrew Allen, and Kouros Owzar|
The score statistic continues to be a fundamental tool for statistical inference. In the analysis of data from high-throughput genomic assays, inference on the basis of the score usually enjoys greater stability, considerably higher computational efficiency, and lends itself more readily to the use of resampling methods than the asymptotically equivalent Wald or likelihood ratio tests. The score function often depends on a set of unknown nuisance parameters which have to be replaced by estimators, but can be improved by calculating the efficient score, which accounts for the variability induced by estimating these parameters. Manual derivation of the efficient score is tedious and error-prone, so we illustrate using computer algebra to facilitate this derivation. We demonstrate this process within the context of a standard example from genetic association analyses, though the techniques shown here could be applied to any derivation, and have a place in the toolbox of any modern statistician. We further show how the resulting symbolic expressions can be readily ported to compiled languages, to develop fast numerical algorithms for high-throughput genomic analysis. We conclude by considering extensions of this approach. The code featured in this report is available online as part of the supplementary material.
|Alternate Journal||Am Stat|
|Original Publication||Facilitating the calculation of the efficient score using symbolic computing.|
|PubMed Central ID||PMC6092959|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Facilitating the Calculation of the Efficient Score Using Symbolic Computing.