Open AccessPosted Content
The S-U Algorithm for Missing Data Problems
Glen A. Satten,Somnath Datta +1 more
12
TL;DR: A new Monte-Carlo method for finding the solution of an estimating equation that can be expressed as the expected value of a ‘full data’ estimating equation in which the expectedvalue is with respect to the distribution of the missing data given the observed data is presented.
read more
Abstract: We present a new Monte-Carlo method for finding the solution of an estimating equation that can be expressed as the expected value of a 'full data' estimating equation in which the expected value is with respect to the distribution of the missing data given the observed data. Equations such as these arise whenever the E-M algorithm can be used. The algorithm alternates between two steps: an S-step, in which the missing data are simulated, either from the conditional distribution described above or from a more convenient importance sampling distribution, and a U-step, in which parameters are updated using a closed-form expression that does not require a numerical maximization. We present two numerical examples to illustrate the method. Theoretical results are obtained establishing consistency and asymptotic normality of the approximate solution obtained by our method.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Marginal regression models for clustered count data based on zero‐inflated Conway–Maxwell–Poisson distribution with applications
TL;DR: Novel modeling incorporating zero inflation, clustering, and overdispersion sheds some new light on the effect of community water fluoridation and other factors.
35
Diseker RA, Peterman TA, Kamb ML, Kent C, Zenilman JM, Douglas JM, Rhodes F, Iatesta M, Project RESPECT Study Group. Circumcision and STD in the United States: cross sectional and cohort analyses. Sexually Transmitted Infections 2000; 76:474-479.
Kenneth L. Dominguez,Jeanne Bertolli,Mary Glenn Fowler,Peters,Sharon K. Melville,Tamara A. Rakusan,Toni Frederick +6 more
- 01 Jan 2000
12
Inference on Clustered Survival Data Using Imputed Frailties
TL;DR: In this article, a method for fitting frailty models to clustered survival data that is intermediate between the fully parametric and nonparametric maximum likelihood estimation approaches is proposed, where a parametric form is assumed for the baseline hazard, but only for the purpose of imputing the unobserved frailties.
•Dissertation
Working with real world datasets: preprocessing and prediction with large incomplete and heterogeneous datasets.
Holger Schöner
- 01 Jan 2005
TL;DR: Two methods are presented which allow to directly handle missing values, i.e. without prior pre-processing, and a statistical model based on entropy maximization (Approximate Maximum Entropy, AME) is presented and refined, which can improve predictions for incomplete data sets.
4
References
•Book
Numerical recipes in Pascal : the art of scientific computing
William H. Press,Brian P. Flannery,Saul A. Teukolsky +2 more
- 01 Jan 1989
13K
•Book
Analysis of longitudinal data
Peter J. Diggle,Patrick J. Heagerty,Kung-Yee Liang,Scott L. Zeger +3 more
- 01 Jan 1994
TL;DR: In this paper, a generalized linear model for longitudinal data and transition models for categorical data are presented. But the model is not suitable for categric data and time dependent covariates are not considered.
7.2K
•Book
Martingale Limit Theory and Its Application
Peter Hall,E Lukacs,Z W Birnbaum,C. C. Heyde +3 more
- 23 Sep 2014
4K
A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms
Greg C. G. Wei,Martin A. Tanner +1 more
TL;DR: Two modifications to the MCEM algorithm (the poor man's data augmentation algorithms), which allow for the calculation of the entire posterior, are presented and serve as diagnostics for the validity of the posterior distribution.
1.6K
Markov Chain Monte Carlo Maximum Likelihood
Charles J. Geyer
- 01 Jan 1991
TL;DR: Markov chain Monte Carlo (MCMC) as discussed by the authors is a general tool for simulation of complex stochastic processes useful in many types of statistical inference, including maximum likelihood estimation and maximum pseudo likelihood estimation.
1.4K