Results 101 to 110 of about 527,420 (298)
Canadian Journal of Statistics, EarlyView.Abstract Homogeneous normalized random measures with independent increments represent a broad class of Bayesian nonparametric priors and thus are widely used. In this article, we obtain the strong law of large numbers, the central limit theorem (CLT), and the functional central limit theorem (fCLT) of such measures when the concentration parameter a ...
Junxi Zhang, Shui Feng, Yaozhong Hu
wiley +1 more source
Bayesian Nonparametric Modeling for Rapid Design of Metamaterial Microstructures
International Journal of Antennas and Propagation, 2014 We consider the problem of rapid design of massive metamaterial (MTM) microstructures from a statistical point of view. A Bayesian nonparametric model, namely, Gaussian Process (GP) mixture, is developed to generate the mapping relationship from the ...
Bin Liu, Chunlin Ji
doaj +1 more source
Posterior analysis for some classes of nonparametric models [PDF]
Recently, James [15, 16] has derived important results for various models in Bayesian nonparametric inference. In particular, he dened a spatial version of neutral to the right processes and derived their posterior distribution. Moreover, he obtained the
Antonio Lijoi +2 more
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Sparse maximum likelihood estimation of regression models
Canadian Journal of Statistics, EarlyView.Abstract For regression model selection and estimation, we study a small set of candidate models of maximum likelihood from which all information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) choose their models.
Min Tsao
wiley +1 more source
Bayesian Analysis for Penalized Spline Regression Using WinBUGS [PDF]
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression ...
Matthew P. Wand +2 more
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Normalized random measures driven by increasing additive processes [PDF]
, 2004 This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes.
PRUENSTER, IGOR +4 more
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The life and work of Ernest Sydney Keeping
Canadian Journal of Statistics, EarlyView.Abstract This article reviews the life and contributions of Ernest S. Keeping (1895–1984), a British‐born physicist who became a mathematics professor at the University of Alberta in 1929. He played a pivotal role in shaping the university's Mathematics Department, serving as its head from 1954 to 1961, and continuing to teach until 1970, nine years ...
David R. Bellhouse, Christian Genest
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Bayesian Non-Parametric Inference for Multivariate Peaks-over-Threshold Models
EntropyWe consider a constructive definition of the multivariate Pareto that factorizes the random vector into a radial component and an independent angular component. The former follows a univariate Pareto distribution, and the latter is defined on the surface
Peter Trubey, Bruno Sansó
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A Bayesian Nonparametric Method for Prediction in EST Analysis [PDF]
In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a) the coverage, defined as the proportion of unique genes in the library represented in the ...
Ramsés H. Mena +2 more
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Model selection for causal inference in missing exposure problems
Canadian Journal of Statistics, EarlyView.Abstract In causal inference, properly selecting the propensity score (PS) model is an important topic and has been widely investigated in observational studies. However, there are very few studies investigating the model selection issue for causal inference when the exposure is missing at random (MAR). When the exposure variable contains missing data,
Yuliang Shi, Yeying Zhu, Joel A. Dubin
wiley +1 more source