25,830 research outputs found
Penalized log-likelihood estimation for partly linear transformation models with current status data
We consider partly linear transformation models applied to current status
data. The unknown quantities are the transformation function, a linear
regression parameter and a nonparametric regression effect. It is shown that
the penalized MLE for the regression parameter is asymptotically normal and
efficient and converges at the parametric rate, although the penalized MLE for
the transformation function and nonparametric regression effect are only
consistent. Inference for the regression parameter based on a block
jackknife is investigated. We also study computational issues and demonstrate
the proposed methodology with a simulation study. The transformation models and
partly linear regression terms, coupled with new estimation and inference
techniques, provide flexible alternatives to the Cox model for current status
data analysis.Comment: Published at http://dx.doi.org/10.1214/009053605000000444 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Quasi regular Dirichlet forms and the stochastic quantization problem
After recalling basic features of the theory of symmetric quasi regular
Dirichlet forms we show how by applying it to the stochastic quantization
equation, with Gaussian space-time noise, one obtains weak solutions in a large
invariant set. Subsequently, we discuss non symmetric quasi regular Dirichlet
forms and show in particular by two simple examples in infinite dimensions that
infinitesimal invariance, does not imply global invariance. We also present a
simple example of non-Markov uniqueness in infinite dimensions
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