Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations

2012 ◽  
Vol 32 (5) ◽  
pp. 833-848 ◽  
Author(s):  
Haocheng Li ◽  
Grace Y. Yi
Keyword(s):  
Binary Data ◽  
Estimation Methods ◽  
Missing Observations ◽  
Longitudinal Binary Data ◽  
Nonignorable Missing

Two‐wave two‐phase outcome‐dependent sampling designs, with applications to longitudinal binary data

2021 ◽  
Author(s):  
Ran Tao ◽  
Nathaniel D. Mercaldo ◽  
Sebastien Haneuse ◽  
Jacob M. Maronge ◽  
Paul J. Rathouz ◽  
...  
Keyword(s):  
Binary Data ◽  
Two Phase ◽  
Longitudinal Binary Data

A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros

Biometrics ◽  
2008 ◽  
Vol 64 (2) ◽  
pp. 611-619 ◽  
Author(s):  
Dimitris Rizopoulos ◽  
Geert Verbeke ◽  
Emmanuel Lesaffre ◽  
Yves Vanrenterghem
Keyword(s):  
Binary Data ◽  
Joint Model ◽  
Excess Zeros ◽  
Longitudinal Binary Data

Latent Gaussian copula models for longitudinal binary data

2021 ◽  
pp. 104940
Author(s):  
Cheng Peng ◽  
Yihe Yang ◽  
Jie Zhou ◽  
Jianxin Pan
Keyword(s):  
Binary Data ◽  
Gaussian Copula ◽  
Copula Models ◽  
Longitudinal Binary Data

scholarly journals Modeling overdispersed longitudinal binary data using a combined beta and normal random-effects model

2012 ◽  
Vol 70 (1) ◽  
Author(s):  
Wondwosen Kassahun ◽  
Thomas Neyens ◽  
Geert Molenberghs ◽  
Christel Faes ◽  
Geert Verbeke
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Model ◽  
Longitudinal Binary Data

scholarly journals Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data

2019 ◽  
Vol 20 (S15) ◽  
Author(s):  
Neo Christopher Chung ◽  
BłaŻej Miasojedow ◽  
Michał Startek ◽  
Anna Gambin
Keyword(s):  
Binary Data ◽  
Hypothesis Test ◽  
Similarity Coefficient ◽  
Statistical Hypothesis ◽  
Estimation Methods ◽  
Unbiased Estimation ◽  
Tanimoto Coefficient ◽  
Tanimoto Similarity ◽  
Biological Studies ◽  
Wide Range

Abstract Background A survey of presences and absences of specific species across multiple biogeographic units (or bioregions) are used in a broad area of biological studies from ecology to microbiology. Using binary presence-absence data, we evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied. Results We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. The exact and asymptotic solutions are derived. To overcome a computational burden due to high-dimensionality, we propose the bootstrap and measurement concentration algorithms to efficiently estimate statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate p-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution, particularly with an increasing dimensionality. We showcase their applications in evaluating co-occurrences of bird species in 28 islands of Vanuatu and fish species in 3347 freshwater habitats in France. The proposed methods are implemented in an open source R package called (https://cran.r-project.org/package=jaccard). Conclusion We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient for binary data, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science.

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