… model’s prediction more effectively. In the second setting we consider a model trained on
biological data … this case ”true to the data” and show that for a sparse model (Lasso regularized…

… state-space methods to construct optimal forecasts and estimates of the underlying true
values; and we show how to optimally estimate trends or cycles from multiple data vintages. …

… Thus, we suggest that the quantitative business objective of a model, measured by its
effectiveness for campaigns, is to maximize (AB) – (CD), which is hereafter referred to as the true …

… posterior distribution of every model parameter for every model.1 These … is the true value
of the parameter, given the assumptions of the model, and the information provided by the data. …

… response patterns in a mixture, and separate variability in the data into components due
to variation of true preference patterns and components due to random error (as in Table 3). …

… model presented in this paper is essentially a discrete data analogue of the familiar continuous
variate distributed lag model … to develop a model applicable to panel data and to develop …

… expectation that the “true model” ought to work the best, provided we know the true model
to begin with. However, one must question whether we ever know the true model in ecology. …

… The sources we identified can serve as a checklist to safely control comparisons, so that
true discrimination capacity is compared as opposed to artefacts arising from data structure, …

… In that there was no data representing true absence of each species in Australia, the
proportions of the extent of Australia identified as suitable were calculated, as an index of potential …

… model performance should not be affected by prevalence or the size of the specific data
set used for model … We propose the true skill statistic (TSS), also known as the Hanssen–Kuipers …