… 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. …

… We develop a model for analysing test–retest data gathered … The model relaxes the
assumption of perfectly correlated true… of equal loadings on the true-score variables representing …

… 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 …

… 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, …

… Intuitively, the model that generated the data … data, this is not necessarily the case and the
“true model” that generated the data is not necessarily the one assigned the strongest model …

… These are: 1) covariant adjustments of the data; 2) standardized gainEach proposed … data;
and 3) true-score adjustor more different strategies through which a given arti- ment of the data…

… 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 …