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A dynamic regime is a function that takes treatment and covariate history and baseline covariates as inputs and returns a decision to be made. Robins (2004) proposed g-estimation using structural nested mean models for making inference about the optimal regime in a multi-interval trial. The method provides clear advantages over traditional parametric(More)
In recent years there has been growing interest in discrimi-native parameter training techniques, resulting from notable improvements in speech recognition performance on tasks ranging in size from digit recognition to Switchboard. Typified by Maximum Mutual Information training, these methods assume a fixed statistical modeling structure, and then optimize(More)
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