Sufficient dimension reduction based on distance‐weighted discrimination
نویسندگان
چکیده
منابع مشابه
Likelihood-based Sufficient Dimension Reduction
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.
متن کاملSufficient Dimension Reduction Summaries
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...
متن کاملTensor sufficient dimension reduction.
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...
متن کاملA note on sufficient dimension reduction
In this paper, we presented a theoretical result and then discussed possible applications of our result to SDR problems. In addition to providing insights into existing SDR methods when Y is univariate; our theorem also applies to multivariate responses, especially when the response takes the form of ðY ;W Þ, where Y is a continuous variable and W is categorical. r 2007 Elsevier B.V. All rights...
متن کاملSufficient dimension reduction and prediction in regression.
Dimension reduction for regression is a prominent issue today because technological advances now allow scientists to routinely formulate regressions in which the number of predictors is considerably larger than in the past. While several methods have been proposed to deal with such regressions, principal components (PCs) still seem to be the most widely used across the applied sciences. We give...
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: Scandinavian Journal of Statistics
سال: 2020
ISSN: 0303-6898,1467-9469
DOI: 10.1111/sjos.12484