Gaussian model selection with an unknown variance
نویسندگان
چکیده
منابع مشابه
Gaussian Model Selection with an Unknown Variance
Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean μ of Y by model selection. More precisely, we start with a collection S = {Sm, m ∈M} of linear subspaces of R and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estimat...
متن کاملGaussian Model Selection with Unknown Variance
Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean of Y by model selection. More precisely, we start with a collection S = {Sm, m ∈M} of linear subspaces of R and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estimator...
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Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean μ of Y by model selection. More precisely, we start with a collection S = {Sm,m ∈ M} of linear subspaces of Rn and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estima...
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Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean µ of Y by model selection. More precisely, we start with a collection S = {Sm, m ∈ M} of linear subspaces of R n and associate to each of these the least-squares estimator of µ on Sm. Then, we use a data driven penalized criterion in order to select one esti...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2009
ISSN: 0090-5364
DOI: 10.1214/07-aos573