58 citations · 122 across the 11 of their papers we have counts for
12 papers
A Regression Tree Method for Longitudinal and Clustered Data with Multivariate Responses
Wenbo Jing, Jeffrey S. Simonoff
RE-EM tree is a tree-based method that combines the regression tree and the linear mixed effects model for modeling univariate response longitudinal or clustered data. In this pape…
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…
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…
Hot Racquet or Not? An Exploration of Momentum in Grand Slam Tennis Matches
Arjun Goyal, Jeffrey S. Simonoff
The presence of momentum in sports, where the outcome of a previous event affects a following event, is a belief often held by fans, and has been the subject of statistical study f…
Ensemble methods for survival function estimation with time-varying covariates
Weichi Yao, Halina Frydman, Denis Larocque +1
Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of survival function. However, the traditional survival forests -…
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…
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…
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…
Survival Trees for Interval-Censored Survival data
Wei Fu, Jeffrey S. Simonoff
Interval-censored data, in which the event time is only known to lie in some time interval, arise commonly in practice; for example, in a medical study in which patients visit clin…
Survival trees for left-truncated and right-censored data, with application to time-varying covariate data
Wei Fu, Jeffrey S. Simonoff
Tree methods (recursive partitioning) are a popular class of nonparametric methods for analyzing data. One extension of the basic tree methodology is the survival tree, which appli…
On the Sensitivity of the Lasso to the Number of Predictor Variables
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
The Lasso is a computationally efficient regression regularization procedure that can produce sparse estimators when the number of predictors (p) is large. Oracle inequalities prov…
Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the po…