Add to ORKGAbstract—Detection of sparse signals arises in a wide range of modern scientific studies. The focus so far has been mainly on Gaussian mixture models. In this paper, we consider the detection problem under a general sparse mixture model and obtain explicit expressions for the detection boundary under mild regularity conditions. Moreover, for Gaussian null hypothesis, we establish the adaptive optimality of the higher criticism procedure for all sparse mixtures satisfying the same conditions. In particular, the general results obtained in this paper recover and extend in a unified manner the previously known results on sparse detection far beyond the conventional Gaussian model and other exponential families. Index Terms—Hypothesis testing, ...
We study the problem of detection of a p-dimensional sparse vector of parameters in the linear regre...
We observe a $N\times M$ matrix $Y_{ij}=s_{ij}+\xi_{ij}$ with $\xi_{ij}\sim\CN(0,1)$ i.i.d. in $i,j$...
For high dimensional statistical models, researchers have begun to fo-cus on situations which can be...
Detection of sparse signals arises in a wide range of modern scientific studies. The focus so far ha...
In this thesis, we study the sparse mixture detection problem as a binary hypothesis testing problem...
The problem of detecting heterogeneous and heteroscedastic Gaussian mixtures is considered. The focu...
Summary. The problem of detecting heterogeneous and heteroscedastic Gaussian mixtures is considered....
Abstract. We consider Gaussian mixture models in high dimensions and concentrate on the twin tasks o...
This thesis focuses on two topics. In the first topic, we consider the problem of detecting sparse h...
International audienceWe consider Gaussian mixture models in high dimensions, focusing on the twin t...
The Donoho and Jin (2004) higher criticism statistic (HC) is an increasingly popular tool in sparse ...
The detection of sparse heterogeneous mixtures becomes important in settings where a small proportio...
Given a heterogeneous Gaussian sequence model with unknown mean $\theta \in \mathbb R^d$ and known c...
Modern statistical research focuses on problems in high-dimensional data analysis. This thesis focus...
We consider the problem of detecting sparse heterogeneous mixtures from a nonparametric perspective,...
We study the problem of detection of a p-dimensional sparse vector of parameters in the linear regre...
We observe a $N\times M$ matrix $Y_{ij}=s_{ij}+\xi_{ij}$ with $\xi_{ij}\sim\CN(0,1)$ i.i.d. in $i,j$...
For high dimensional statistical models, researchers have begun to fo-cus on situations which can be...
Detection of sparse signals arises in a wide range of modern scientific studies. The focus so far ha...
In this thesis, we study the sparse mixture detection problem as a binary hypothesis testing problem...
The problem of detecting heterogeneous and heteroscedastic Gaussian mixtures is considered. The focu...
Summary. The problem of detecting heterogeneous and heteroscedastic Gaussian mixtures is considered....
Abstract. We consider Gaussian mixture models in high dimensions and concentrate on the twin tasks o...
This thesis focuses on two topics. In the first topic, we consider the problem of detecting sparse h...
International audienceWe consider Gaussian mixture models in high dimensions, focusing on the twin t...
The Donoho and Jin (2004) higher criticism statistic (HC) is an increasingly popular tool in sparse ...
The detection of sparse heterogeneous mixtures becomes important in settings where a small proportio...
Given a heterogeneous Gaussian sequence model with unknown mean $\theta \in \mathbb R^d$ and known c...
Modern statistical research focuses on problems in high-dimensional data analysis. This thesis focus...
We consider the problem of detecting sparse heterogeneous mixtures from a nonparametric perspective,...
We study the problem of detection of a p-dimensional sparse vector of parameters in the linear regre...
We observe a $N\times M$ matrix $Y_{ij}=s_{ij}+\xi_{ij}$ with $\xi_{ij}\sim\CN(0,1)$ i.i.d. in $i,j$...
For high dimensional statistical models, researchers have begun to fo-cus on situations which can be...