1,721,187 research outputs found
sj-pdf-2-smm-10.1177_09622802211060525 - Supplemental material for Mid-quantile regression for discrete responses
No full textSupplemental material, sj-pdf-2-smm-10.1177_09622802211060525 for Mid-quantile regression for discrete responses by Marco Geraci and Alessio Farcomeni in Statistical Methods in Medical Research</p
SMM903763 Supplemental Material - Supplemental material for A family of linear mixed-effects models using the generalized Laplace distribution
No full textSupplemental material, SMM903763 Supplemental Material for A family of linear mixed-effects models using the generalized Laplace distribution by Marco Geraci and Alessio Farcomeni in Statistical Methods in Medical Research</p
Quantile-distribution Functions and Their Use for Classification
No full textWe develop a flexible parametric framework for the estimation of quantile functions.
The method involves the specification of an analytical quantile distribution function
for the data at hand [1]. We focus on quantile functions that are linear with respect
to their parameters, such as the flattened generalized logistic distribution [2]: these
can adapt to a wide range of distributional shapes and allow for the estimation to be
carried out through a computationally efficient least-squares method based on the
order statistics.
Inferential properties of this estimator, such as its asymptotic distribution, are derived, and these allow for the definition of a test of hypothesis for the equality of two
distributions. The properties of the test are evaluated via a simulation study.
Our method of quantile function estimation is implemented as a density estimation
method in the naïve Bayes classifier. This innovation is compared to standard approaches for the classifier in a simulation study, and is illustrated on a real data set
coming from microRNA profiling in human Medulloblastoma. Moreover, the test of
hypothesis is shown to be useful as a variable selection method
On the severity of COVID‐19 infections in 2021 in Italy
Full text linkLetter to the editor about the proper indicators to be used in monitoring COVID-19 after vaccination of the population majority
Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data
No full textA Bayesian approach is developed for selecting the model that is most supported by the data within a class of marginal models for categorical variables, which are formulated through equality and/or inequality constraints on generalized logits (local, global, continuation, or reverse continuation), generalized log-odds ratios, and similar higher-order interactions. For each constrained model, the prior distribution of the model parameters is specified following the encompassing prior approach. Then, model selection is performed by using Bayes factors estimated through an importance sampling method. The approach is illustrated by three applications based on different datasets, which also include explanatory variables. In connection with one of these examples, a sensitivity analysis to the prior specification is also performed. (C) 2012 Elsevier B.V. All rights reserved
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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