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20182021
most citedSelection of Regression Models under Linear Restrictions for Fixed and Random Designs

2 citations · 3 across the 2 of their papers we have counts for

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5 papers · 1 filter

stat.ME2021

Dynamic estimation with random forests for discrete-time survival data

Hoora Moradian, Weichi Yao, Denis Larocque +2

Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we in…

stat.ME20202 cited

Selection of Regression Models under Linear Restrictions for Fixed and Random Designs

Sen Tian, Clifford M. Hurvich, Jeffrey S. Simonoff

Many important modeling tasks in linear regression, including variable selection (in which slopes of some predictors are set equal to zero) and simplified models based on sums or d…

stat.ME2019

On the Use of Information Criteria for Subset Selection in Least Squares Regression

Sen Tian, Clifford M. Hurvich, Jeffrey S. Simonoff

Least squares (LS)-based subset selection methods are popular in linear regression modeling. Best subset selection (BS) is known to be NP-hard and has a computational cost that gro…

stat.ME20191 cited

An Ensemble Method for Interval-Censored Time-to-Event Data

Weichi Yao, Halina Frydman, Jeffrey S. Simonoff

Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known…

stat.ME2018

Joint Latent Class Trees: A Tree-Based Approach to Modeling Time-to-event and Longitudinal Data

Ningshan Zhang, Jeffrey S. Simonoff

In this paper, we propose a semiparametric, tree based joint latent class modeling approach (JLCT) to model the joint behavior of longitudinal and time-to-event data. Existing join…