• Corpus ID: 64806089

Regression Analysis of Proportion Outcomes with Random Effects

  title={Regression Analysis of Proportion Outcomes with Random Effects},
  author={Colman Humphrey and Daniel Swingley},
  journal={arXiv: Methodology},
A regression method for proportional, or fractional, data with mixed effects is outlined, designed for analysis of datasets in which the outcomes have substantial weight at the bounds. In such cases a normal approximation is particularly unsuitable as it can result in incorrect inference. To resolve this problem, we employ a logistic regression model and then apply a bootstrap method to correct conservative confidence intervals. This paper outlines the theory of the method, and demonstrates its… 

Figures from this paper

A cross-linguistic examination of toddlers' interpretation of vowel duration.
The results suggest a strong capacity for phonetic analysis in children before their second birthday, and find that word recognition in Dutch toddlers was affected by shortening but not lengthening of vowels, matching an asymmetry also found in Dutch adults.
Bilingualism Affects Infant Cognition: Insights From New and Open Data
Abstract Bilingualism has been hypothesized to shape cognitive abilities across the lifespan. Here, we examined the replicability of a seminal study that showed monolingual–bilingual differences in


Models of accuracy in repeated-measures designs
Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates
We offer simple quasi-likelihood methods for estimating regression models with a fractional dependent variable and for performing asymptotically valid inference. Compared with log-odds type
Uncertainty in online experiments with dependent data: an evaluation of bootstrap methods
This work develops a framework for understanding how dependence affects uncertainty in user-item experiments and evaluates how bootstrap methods that account for differing levels of dependence perform in practice, and highlights the importance of analysis of inferential methods for complex dependence structures common to online experiments.
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal
An Introduction to the Bootstrap
15 Empirical Bayes Method, 2nd edition J.S. Maritz and T. Lwin (1989) Symmetric Multivariate and Related Distributions K.-T. Fang, S. Kotz and K. Ng (1989) Ieneralized Linear Models, 2nd edition P.
Resampling and exchangeable arrays
The nonparametric, or resampling, bootstrap for a single unstructured sample corresponds to the algebraic operation of monoid composition, with a uniform distribution on the monoid. With this
Estimators obtained by maximizing a likelihood function are studied in the case where the true p.d.f. does not necessarily belong to the family chosen for the likelihood function. When such a
What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum
A deeper understanding of bootstrapping methods is provided—how they work, when they work or not, and which methods work better—and pedagogical issues are highlighted.
Knowledge Discovery and Data Mining: An Overview
The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for