Zobrazeno 1 - 10
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pro vyhledávání: '"Wand, M."'
Autor:
Wand, M. P., Yu, J. C. F.
We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate
Externí odkaz:
http://arxiv.org/abs/2009.06182
Autor:
Maestrini, L., Wand, M. P.
Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models. The notion of a factor graph fragment allows for compartmentalization of algebra and computer code. We
Externí odkaz:
http://arxiv.org/abs/2005.09876
A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and h
Externí odkaz:
http://arxiv.org/abs/1903.04043
Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to approximate
Externí odkaz:
http://arxiv.org/abs/1805.08423
Autor:
Wand, M. P.
We show how the notion of message passing can be used to streamline the algebra and computer coding for fast approximate inference in large Bayesian semiparametric regression models. In particular, this approach is amenable to handling arbitrarily la
Externí odkaz:
http://arxiv.org/abs/1602.07412
Publikováno v:
Annals of Statistics 2011, Vol. 39, No. 5, 2502-2532
We derive the precise asymptotic distributional behavior of Gaussian variational approximate estimators of the parameters in a single-predictor Poisson mixed model. These results are the deepest yet obtained concerning the statistical properties of a
Externí odkaz:
http://arxiv.org/abs/1202.5183
Autor:
Samworth, R. J., Wand, M. P.
Publikováno v:
Annals of Statistics 2010, Vol. 38, No. 3, 1767-1792
We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in-bandwidth asymptotic approximation to a risk that is appropriate for HDR estimation. This approximation is then used to de
Externí odkaz:
http://arxiv.org/abs/1010.0591
Publikováno v:
Electronic Journal of Statistics 2008, Vol. 2, 916-938
We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely difficult,
Externí odkaz:
http://arxiv.org/abs/0810.1163
Autor:
Wand, M. P., Ormerod, J. T.
This is an expos\'e on the use of O'Sullivan penalised splines in contemporary semiparametric regression, including mixed model and Bayesian formulations. O'Sullivan penalised splines are similar to P-splines, but have an advantage of being a direct
Externí odkaz:
http://arxiv.org/abs/0707.0143
Publikováno v:
Statistical Science 2006, Vol. 21, No. 1, 35-51
Linear mixed models are able to handle an extraordinary range of complications in regression-type analyses. Their most common use is to account for within-subject correlation in longitudinal data analysis. They are also the standard vehicle for smoot
Externí odkaz:
http://arxiv.org/abs/math/0606491