2005 TrevorHastie Bio

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Subject Headings: Generalized Additive Model, Linear Principal Components, Coordinate Function.


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  • I am an applied statistician, with a joint position in Biostatistics in the medical school. Most of my research is generated from applications with which I am involved. I tend to teach applied classes as well. For example I have taught parts of the 315,316 Ph.D first year graduate series, as well as 341, an applied multivariate class, and 315, our new Modern Applied Statistics class.
  • My research has focussed on function approximation and curve fitting within a variety of different applications. A common theme in my research so far has been to try and provide methodology that naturally bridges the gap between the traditional well tested linear techniques, and the newer more adventurous nonparametric frontiers.
  • Generalized additive models adapt nonparametric regression technology to provide more flexibility to the usual linear models used in applied areas, such as logistic and log-linear models. Principal curves and surfaces generalize linear principal components by allowing nonlinear coordinate functions. Currently I am interested in flexible methods for classification, and my research is focussed on developing richer classes of models for this task, as well as to understand better the nature of the classification problem.
  • Sometimes even the linear techniques are too rich, such as when the variables are sampled versions of a smooth function or image. The exponential growth in computer processing speed and storage has allowed us to routinely gather data where each observation is one or more digitized image. This has lead to a new field currently known as functional data analysis, and is filled with many interesting (open) problems, because the traditional techniques no longer work.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2005 TrevorHastie BioTrevor Hastiehttp://www-stat.stanford.edu/brochure/part4.html#hastie2005