Spatial cross-validation and average-error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple crossvalidation methodology is described, and the relative abilities of three, dimensioned error statistics—the root-mean-square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator...

Many data analysis applications deal with large matrices and involve approximating the matrix using a small number of “components.” Typically, these components are linear combinations of the rows and columns of the matrix, and are thus difficult to interpret in terms of the original features of the input data. In this paper, we propose and study matrix approximations that are explicitly express...

Multiplicative regression model or accelerated failure time model, which becomes linear regression model after logarithmic transformation, is useful in analyzing data with positive responses, such as stock prices or life times, that are particularly common in economic/financial or biomedical studies. Least squares or least absolute deviation are among the most widely used criterions in statisti...

We examine methods to estimate the average and variance of test error rates over a set of classi ers We begin with the process of drawing a classi er at random for each example Given validation data the average test error rate can be estimated as if validating a single classi er Given the test example inputs the variance can be computed exactly Next we consider the process of drawing a classi e...