In principle, one could use other classification models, for example linear discriminant analysis (LDA), a standard classification algorithm that combines a model such as (7) with a marginal model for (x_{t}.) However, Hastie et al. (2009) have argued that it is often preferable to stick to a model such as (7) rather than rely on LDA in practice.
For example, one could use information on traced cases as it accrues to sequentially estimate the generation interval.
40 For example, one could use our results as further support for not using livers from DCD donors for patients with a low risk of death on the waiting list as these recipients are able to wait for a graft that is more likely to have a favourable long-term outcome.
For example, one could use a forward or backward stepwise strategy, or perform model comparison via the Akaike Information Criterion AICC), in order to identify the single best-fitting model.
For example, one could use the response to lithium prophylaxis as an approximation and a proxy for the classical manic-depressive illness, including in particular episodic course, lack of psychiatric comorbidity, and positive family history.
For example, one could use machine-learning software to predict whether or not people are fast swimmers.
For example, one could use total aqueous arsenic as a measure of the extent of reaction.
For example, one could use market share as an alternative, or survey methods, to evaluate attractiveness subjectively.
For example, one could use the following generalization of (4): g(x)sim d_{a}x^{alpha },quad xto a+0,qquad v(x)sim d_{b}x^{ beta },quad xto b-0, for some fixed (alpha, beta >-1/2) and (d_{a}d_{b}ne 0).
For example, one could use blockwise data to fit scenarios of population expansion, serial bottlenecks, or more general models of multiple merger coalescence (Coop and Ralph 2012).
