A selective review of sufficient dimension reduction for multivariate response regression
نویسندگان
چکیده
We review sufficient dimension reduction (SDR) estimators with multivariate response in this paper. A wide range of SDR methods are characterized as inverse regression or forward estimators. The family includes pooled marginal estimators, projective resampling and distance-based Ordinary least squares, partial semiparametric on the other hand, discussed from family.
منابع مشابه
Moment Based Dimension Reduction for Multivariate Response Regression
Dimension reduction aims to reduce the complexity of a regression without requiring a pre-specified model. In the case of multivariate response regressions, covariance-based estimation methods for the k-th moment based dimension reduction subspaces circumvent slicing and nonparametric estimation so that they are readily applicable to multivariate regression settings. In this article, the covari...
متن کاملSufficient dimension reduction for the conditional mean with a categorical predictor in multivariate regression
Recent sufficient dimension reduction methodologies in multivariate regression do not have direct application to a categorical predictor. For this, we define the multivariate central partial mean subspace and propose two methodologies to estimate it. The first method uses the ordinary least squares. Chi-squared distributed statistics for dimension tests are constructed, and an estimate of the t...
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 کاملSufficient Dimension Reduction via Inverse Regression: A Minimum Discrepancy Approach
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimizing a quadratic objective function. An optimal member of this family, the inverse regression estimator (IRE), is proposed, along with inference methods and a computational algorithm. The IRE has at least three desirable properties: (1) Its estimated basis of the central dimension reduction subspa...
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 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: Journal of Statistical Planning and Inference
سال: 2023
ISSN: ['1873-1171', '0378-3758']
DOI: https://doi.org/10.1016/j.jspi.2023.02.003