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Graph cluster randomization: network exposure to multiple universes
Graph cluster randomization network exposure multiple universes
2013/6/17
A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the ove...
Clustering and Classification via Cluster-Weighted Factor Analyzers
Cluster-weighted models factor analysis mixturemodels parsimonious models
2012/11/23
In model-based clustering and classification, the cluster-weighted model constitutes a convenient approach when the random vector of interest constitutes a response variable Y and a set p of explanato...
Maximum Likelihood Estimation of Gaussian Cluster Weighted Models and Relationships with Mixtures of Regression
Cluster-weighted modeling finite mixtures of regression EM-algorithm
2012/9/19
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
Flexible Mixture Modeling with the Polynomial Gaussian Cluster-Weighted Model
Mixture of distributions Mixture of regressions Polynomial regression Model-based clustering Model-based classification Cluster-weighted models.
2012/9/18
In the mixture modeling frame, this paper presents the polynomial Gaussian cluster-weighted model (CWM). It extends the linear Gaussian CWM, for bivariate data, in a twofold way. Firstly, it allows fo...
Pruning nearest neighbor cluster trees
cluster trees cluster structure subgraphs of a k-NN graph
2011/6/16
Nearest neighbor (k-NN) graphs are widely used
in machine learning and data mining applications,
and our aim is to better understand what
they reveal about the cluster structure of the unknown
und...
Detection of an anomalous cluster in a network
Detecting a cluster of nodes in a network minimax detection Bayesian detection scanstatistic generalized likelihood-ratio test
2010/3/9
We consider the problem of detecting whether or not in a given sensor network, there
is a cluster of sensors which exhibit an “unusual behavior.” Formally, suppose we are given a set of
nodes and at...
Law of the iterated logarithm - cluster points of deterministic and random subsequences
Law of the iterated logarithm cluster points of deterministic random subsequences
2009/9/24
Law of the iterated logarithm - cluster points of deterministic and random subsequences。
The cluster set of {S,(2nLLn)-1/2; EN) in Banach spaces。
Model-Based Inferences from Adaptive Cluster Sampling
Informative sampling MCMC spatial sampling zero-inated count data
2009/9/22
Adaptive cluster sampling is useful for exploring populations of rare
plant and animal species which cluster together because it allows sampling eort
to be concentrated in areas where observed value...
Cluster Allocation Design Networks
Cluster allocation Inuence diagrams Causal inference Identication of policy effects DAGs
2009/9/22
Design Networks expand this frame-
work by including experimental design decision nodes (Madrigal and Smith 2004).
They provide semantics to discuss how a design decision strategy (such as a clus-
...
Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables
EM algorithm High-dimension but low-sample size L1 penalization Microarray gene expression Mixture model Penalized likelihood
2009/9/16
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise...
A note on percolation on Zd：isoperimetric profile via exponential cluster repulsion
percolation parameters exponential cluster repulsion
2009/3/20
We show that for all p>p_c(Z^d) percolation parameters, the probability that the cluster of the origin is finite but has at least t vertices at distance one from the infinite cluster is exponentially ...