Search Results - subject:"Sliced Inverse Regression"

A new sliced inverse regression method for multivariate response

Coudret, R.; Girard, S.; Saracco, J. - In: Computational Statistics & Data Analysis 77 (2014) C, pp. 285-299

considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension reduction …

Persistent link: https://www.econbiz.de/10010871397

Sliced inverse regression for survival data

Shevlyakova, Maya; Morgenthaler, Stephan - In: Statistical Papers 55 (2014) 1, pp. 209-220

We apply the univariate sliced inverse regression to survival data. Our approach is different from the other papers on …

Persistent link: https://www.econbiz.de/10010848070

A sliced inverse regression approach for data stream

Chavent, Marie; Girard, Stéphane; Kuentz-Simonet, Vanessa - In: Computational Statistics 29 (2014) 5, pp. 1129-1152

the sliced inverse regression (SIR) method. But in practice, some disadvantages appear such as the storage of the blocks …

Persistent link: https://www.econbiz.de/10010998439

Entropy-based sliced inverse regression

Hino, Hideitsu; Wakayama, Keigo; Murata, Noboru - In: Computational Statistics & Data Analysis 67 (2013) C, pp. 105-114

structure of complex data. Sliced inverse regression is a well-known dimension reduction method for regression, which assumes an … eigenvalue problem. Sliced inverse regression is based on the strong assumptions on the data distribution and the form of …

Persistent link: https://www.econbiz.de/10010871383

Sparse sufficient dimension reduction using optimal scoring

Wang, Tao; Zhu, Lixing - In: Computational Statistics & Data Analysis 57 (2013) 1, pp. 223-232

sample size. The new procedure is based on the optimal scoring interpretation of the sliced inverse regression method. As a …

Persistent link: https://www.econbiz.de/10010871441

Sufficient dimension reduction in multivariate regressions with categorical predictors

Hilafu, Haileab; Yin, Xiangrong - In: Computational Statistics & Data Analysis 63 (2013) C, pp. 139-147

In this paper, we present a novel sufficient dimension reduction method for multivariate regressions with categorical predictors. We adopt ideas from a previous work byChiaromonte et al. (2002) who proposed sufficient dimension reduction in regressions with categorical predictors and the work...

Persistent link: https://www.econbiz.de/10011056411

High-dimensional regression analysis with treatment comparisons

Lue, Heng-Hui; You, Bing-Ran - In: Computational Statistics 28 (2013) 3, pp. 1299-1317

propose a nonparametric estimation approach based on partial sliced inverse regression (SIR) (Chiaromonte et al. in Ann Stat …

Persistent link: https://www.econbiz.de/10010998525

On determining the structural dimension via directional regression

Yu, Zhou; Dong, Yuexiao; Guo, Ranwei - In: Statistics & Probability Letters 83 (2013) 4, pp. 987-992

Specifying the structural dimension is an important first step for the sufficient dimension reduction methodology. Based on the popular sequential test approach, we propose a novel test statistic via directional regression to determine the structural dimension in this paper.

Persistent link: https://www.econbiz.de/10010662323

Forecasting China’s inflation in a data-rich environment

Lin, Chin-yi; Wang, Chun - In: Applied economics 45 (2013) 19/21, pp. 3049-3057

Persistent link: https://www.econbiz.de/10010192323

A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches

Liquet, Benoît; Saracco, Jérôme - In: Computational Statistics 27 (2012) 1, pp. 103-125

Persistent link: https://www.econbiz.de/10010847677