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Missing data mechanisms and their implications on the analysis of categorical data
(SPRINGER, 2011)
We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to ...
Influence Assessment in an Heteroscedastic Errors-in-Variables Model
(TAYLOR & FRANCIS INC, 2012)
The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential ...
Incomplete septal cirrhosis: an enigmatic disease
(Blackwell Munksgaard, 2014)
Fitting models of continuous trait evolution to incompletely sampled comparative data using approximate bayesian computation
In recent years, a suite of methods has been developed to fit multiple rate models to phylogenetic comparative data. However, most methods have limited utility at broad phylogenetic scales because they typically require ...
On the dynamical incompleteness of the Protein Data Bank
(Oxford University Press, 2017-07)
Major scientific challenges that are beyond the capability of individuals need to be addressed by multi-disciplinary and multi-institutional consortia. Examples of these endeavours include the Human Genome Project, and ...
A short-term deep learning model for urban pollution forecasting with incomplete data
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-01-15)
A deep neural network model for the short term prediction of Ozone, 10 micrometers particulate matter and 2.5 micrometers particulate matter concentrations in a major northwestern metropolitan area of Mexico is developed. ...
On estimation and influence measures for the Negative Binomial regression model based on Q-function
(TAYLOR & FRANCIS INC, 2021)
In this paper the influence measures for the Negative Binomial regression model are presented. Based on the conditional expectation of the complete-data log-likelihood function we derive some influence measures, such as ...