Thomas Richardson

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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. Murphy (2003, Journal of the Royal Statistical Society, Series B 65, 331-366) and Robins (2004, Proceedings of the Second Seattle Symposium on Biostatistics, 189-326) have proposed models and developed semiparametric(More)
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 discriminative 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)
BACKGROUND Objective: To determine to what extent each trial met criteria specified in three research frameworks for ethical trial conduct. Design: Systematic review and narrative analysis. METHODS AND FINDINGS Data sources: MEDBASE and EMBASE databases were searched using a specific search strategy. The Cochrane database for systematic reviews, the(More)
Sequential techniques for the canonical blind deconvolution problem have attracted the attention of computational Bayesians such as Liu and Chen (1995) who applied Sequential Importance Sampling (SIS) to this problem. Subsequently, several extensions have been proposed (e.g. Rejuvenation, Rejection Control, Fixed-Lag Smoothing, Metropolis-Hastings(More)