The bias-variance trade-off is crucial for achieving an optimally performing supervised learning model. Bias is the difference between the average prediction of a model and the actual value. The ...

We present in this paper general formulas for deriving the maximum likelihood estimates and the asymptotic variance-covariance matrix of the positions and effects of quantitative trait loci (QTLs) in ...

A measure of variation for categorical data is discussed. We develop an analysis of variance for a one-way table, where the response variable is categorical. The data can be viewed alternatively as ...

To offset the typically poor estimation of variance and to increase statistical power, investigators have developed methods that borrow information from genes across the array.