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You searched for: Journal Computational statistics & data analysis Remove constraint Journal: Computational statistics & data analysis Publication Year 2019 Remove constraint Publication Year: 2019 Source 2019 v.140 Remove constraint Source: 2019 v.140 Subject regression analysis Remove constraint Subject: regression analysis
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1. Robust sufficient dimension reduction via ball covariance

Author:
Zhang, Jia; Chen, Xin
Source:
Computational statistics & data analysis 2019 v.140 pp. 144-154
ISSN:
0167-9473
Subject:
covariance; model validation; regression analysis; simulation models
Abstract:
... Sufficient dimension reduction is an important branch of dimension reduction, which includes variable selection and projection methods. Most of the sufficient dimension reduction methods are sensitive to outliers and heavy-tailed predictors, and require strict restrictions on the predictors and the response. In order to widen the applicability of sufficient dimension reduction, we propose BCov-SDR ...
DOI:
10.1016/j.csda.2019.06.004

2. Semiparametric regression analysis for left-truncated and interval-censored data without or with a cure fraction

Author:
Shen, Pao-sheng; Chen, Hsin-Jen; Pan, Wen-Harn; Chen, Chyong-Mei
Source:
Computational statistics & data analysis 2019 v.140 pp. 74-87
ISSN:
0167-9473
Subject:
algorithms; cardiovascular diseases; cohort studies; data collection; models; regression analysis; risk factors; statistical inference; variance
Abstract:
... Interval censoring and truncation arise often in cohort studies, longitudinal and sociological research. In this article, we formulate the effects of covariates on left-truncated and mixed case interval-censored (LTIC) data without or with a cure fraction through a general class of semiparametric transformation models. We propose the conditional likelihood approach for statistical inference. For d ...
DOI:
10.1016/j.csda.2019.06.006