An overview of composite likelihood methods
A survey of recent developments in the theory and application of composite likelihood is
provided, building on the review paper of Varin (2008). A range of application areas …
provided, building on the review paper of Varin (2008). A range of application areas …
Missing data: An update on the state of the art.
CK Enders - Psychological Methods, 2023 - psycnet.apa.org
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
[BOOK][B] Applied logistic regression
A new edition of the definitive guide to logistic regression modeling for health science and
other applications This thoroughly expanded Third Edition provides an easily accessible …
other applications This thoroughly expanded Third Edition provides an easily accessible …
Hyperimpute: Generalized iterative imputation with automatic model selection
Consider the problem of imputing missing values in a dataset. One the one hand,
conventional approaches using iterative imputation benefit from the simplicity and …
conventional approaches using iterative imputation benefit from the simplicity and …
A model-based imputation procedure for multilevel regression models with random coefficients, interaction effects, and nonlinear terms.
Despite the broad appeal of missing data handling approaches that assume a missing at
random (MAR) mechanism (eg, multiple imputation and maximum likelihood estimation) …
random (MAR) mechanism (eg, multiple imputation and maximum likelihood estimation) …
Multiple imputation: a review of practical and theoretical findings
JS Murray - 2018 - projecteuclid.org
Multiple imputation is a straightforward method for handling missing data in a principled
fashion. This paper presents an overview of multiple imputation, including important …
fashion. This paper presents an overview of multiple imputation, including important …
[BOOK][B] Bayesian biostatistics
E Lesaffre, AB Lawson - 2012 - books.google.com
The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …
considerable technical innovation in both methodology and computational practicality. One …
Dynamic embeddings for language evolution
Word embeddings are a powerful approach for unsupervised analysis of language.
Recently, Rudolph et al. developed exponential family embeddings, which cast word …
Recently, Rudolph et al. developed exponential family embeddings, which cast word …
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation
Matrix factorization (MF) collaborative filtering is an effective and widely used method in
recommendation systems. However, the problem of finding an optimal trade-off between …
recommendation systems. However, the problem of finding an optimal trade-off between …
[BOOK][B] Adaptive treatment strategies in practice: planning trials and analyzing data for personalized medicine
MR Kosorok, EEM Moodie - 2015 - SIAM
The study of new medical treatments, and sequences of treatments, is inextricably linked
with statistics. Without statistical estimation and inference, we are left with case studies and …
with statistics. Without statistical estimation and inference, we are left with case studies and …