Estimation of the mean function with panel count data using monotone polynomial splines

By Minggen Lu, Ying Zhang, Jian Huang. 2007

131 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2b4b31a40e5387da76f54756ea4fe12f14668db2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jian Huang
2. Lei Hua
3. Minggen Lu
4. Ying Zhang
5. Nicolai Meinshausen
6. Cun-Hui Zhang
7. Tong Zhang
8. Heng Lian

In Finance

1. Lei Hua
2. Jian Huang
3. Ying Zhang

In Statistics and Probability

1. Jian Huang
2. Minggen Lu
3. Lei Hua
4. Ying Zhang
5. Heng Lian
6. Cun-Hui Zhang
7. Nicolai Meinshausen
8. Tong Zhang

Citations in the corpus

• A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data (2010, methodology): “Lu et al. (2007) adopted Zhang–Jamshidian’s algorithm with W =−H to compute the monotone mean function estimates in the context of spline-based sieve non-parametric maximum likelihood analysis of panel count data and demonstrated the numerical advantage of this algorithm over the ICM algorithm used…”
• Semiparametric Estimation Methods for Panel Count Data Using Monotone B-Splines (2009, background): “Lu, Zhang, and Huang (2007) studied nonparametric likelihood-based estimators of the mean function with panel count data using monotone B-splines.”