1,721,317 research outputs found
A correction to make Chao estimator conservative when the number of sampling occasions is finite
No full textChao estimator is not guaranteed to be asymptotically conservative with finite sampling efforts. A simple correction solves the issue. We illustrate with simulations and examples that the corrected Chao estimator is asymptotically conservative, and has lower standard error
A probabilistic approach to the estimation of principal components with nonignorable missing data: Applications in accelerometer-based physical activity studies
No full textA spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence
Full text linkWe propose a model based on discrete latent variables, which are spatially associated and time specific, for the analysis of incident cases of SARS-CoV-2 infections. We assume that for each area the sequence of latent variables across time follows a Markov chain with initial and transition probabilities that also depend on latent variables in neighboring areas. The model is estimated by a Markov chain Monte Carlo algorithm based on a data augmentation scheme, in which the latent states are drawn together with the model parameters for each area and time. As an illustration we analyze incident cases of SARS-CoV-2 collected in Italy at regional level for the period from February 24, 2020, to January 17, 2021, corresponding to 48 weeks, where we use number of swabs as an offset. Our model identifies a common trend and, for every week, assigns each region to one among five distinct risk groups
Alessio Farcomeni and Marco Geraci's contribution to the 'First Discussion Meeting on Statistical Aspects of the Covid-19 Pandemic'
No full textIdentifiability of Population Size from Capture-Recapture Data with Heterogeneous Detection Probabilities
No full textTecnical Report #
An overview of robust methods in medical research
No full textRobust statistics is an extension of classical parametric statistics that specifically takes into account the fact that the assumed parametric models used by the researchers are only approximate. In this article, we review and outline how robust inferential procedures may routinely be applied in practice in the biomedical research. Numerical illustrations are given for the t-test, regression models, logistic regression, survival analysis and ROC curves, showing that robust methods are often more appropriate than standard procedures
Recapture models under equality constraints for the conditional capture probabilities
No full textWe introduce a general class of capture-recapture models in which capture probabilities depend on capture history. We discuss constrained versions of the saturated model based on equality constraints. Inference can be performed through a simple estimating equation. The approach is illustrated on a dataset concerning Great Copper butterflies in Willamette Valley of Oregon. Copyright 2011, Oxford University Press.
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