Multivariate spatial nonparametric modelling via kernel processes mixing
نویسندگان
چکیده
منابع مشابه
Multivariate spatial nonparametric modelling via kernel processes mixing.
In this paper we develop a nonparametric multivariate spatial model that avoids specifying a Gaussian distribution for spatial random effects. Our nonparametric model extends the stick-breaking (SB) prior of Sethuraman (1994), which is frequently used in Bayesian modelling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended here to the spatial setti...
متن کاملMultivariate kernel partition processes
This article considers the problem of accounting for unknown multivariate mixture distributions within Bayesian hierarchical models motivated by functional data analysis. Most nonparametric Bayes methods rely on global partitioning, with subjects assigned to a single cluster index for all their random effects. We propose a multivariate kernel partition process (KPP) that instead allows the clus...
متن کاملNonparametric Density Estimation via Diffusion Mixing
Suppose we are given empirical data and a prior density about the distribution of the data. We wish to construct a nonparametric density estimator that incorporates the prior information. We propose an estimator that allows for the incorporation of prior information in the density estimation procedure within a non-Bayesian framework. The prior density is mixed with the available empirical data ...
متن کاملNonparametric Tree Graphical Models via Kernel Embeddings
We introduce a nonparametric representation for graphical model on trees which expresses marginals as Hilbert space embeddings and conditionals as embedding operators. This formulation allows us to define a graphical model solely on the basis of the feature space representation of its variables. Thus, this nonparametric model can be applied to general domains where kernels are defined, handling...
متن کاملMann - Withney multivariate nonparametric control chart.
In many quality control applications, the necessary distributional assumptions to correctly apply the traditional parametric control charts are either not met or there is simply not enough information or evidence to verify the assumptions. It is well known that performance of many parametric control charts can be seriously degraded in situations like this. Thus, control charts that do not requi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistica Sinica
سال: 2013
ISSN: 1017-0405
DOI: 10.5705/ss.2011.172