Dimension estimation in sufficient dimension reduction: A unifying approach
نویسندگان
چکیده
منابع مشابه
Efficient Estimation in Sufficient Dimension Reduction.
We develop an efficient estimation procedure for identifying and estimating the central subspace. Using a new way of parameterization, we convert the problem of identifying the central subspace to the problem of estimating a finite dimensional parameter in a semiparametric model. This conversion allows us to derive an efficient estimator which reaches the optimal semiparametric efficiency bound...
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Observational studies assessing causal or non-causal relationships between an explanatory measure and an outcome can be complicated by hosts of confounding measures. Large numbers of confounders can lead to several biases in conventional regression based estimation. Inference is more easily conducted if we reduce the number of confounders to a more manageable number. We discuss use of sufficien...
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Tensor is a multiway array. With the rapid development of science and technology in the past decades, large amount of tensor observations are routinely collected, processed, and stored in many scientific researches and commercial activities nowadays. The colorimetric sensor array (CSA) data is such an example. Driven by the need to address data analysis challenges that arise in CSA data, we pro...
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Sufficient dimension reduction provides a useful tool to study the dependence between a response Y and a multidimensional regressor X , sliced regression (Wang and Xia, 2008) being reported to have a range of advantages – estimation accuracy, exhaustiveness and robustness – over many other methods. A new formulation is proposed here based on the Hellinger integral of order two – and so jointly ...
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We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2011
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2010.08.007