Francesco Innocenti

Francesco Innocenti
Maastricht University | UM · Department of Methodology and Statistics

PhD in Statistics

About

4
Publications
780
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10
Citations
Additional affiliations
January 2021 - present
Maastricht University
Position
  • Professor (Assistant)
Description
  • https://cris.maastrichtuniversity.nl/en/persons/francesco-innocenti
September 2016 - May 2021
Maastricht University
Position
  • PhD Student
Description
  • Title of the PhD project: Optimal Survey Sampling for Prevalence Estimation and Prediction Modeling https://doi.org/10.26481/dis.20210520fi
Education
December 2013 - February 2016
University of Florence
Field of study
  • Statistics, Actuarial and Financial Sciences
September 2010 - December 2013
University of Florence
Field of study
  • Economics

Publications

Publications (4)
Article
To prevent mistakes in psychological assessment, the precision of test norms is important. This can be achieved by drawing a large normative sample and using regression-based norming. Based on that norming method, a procedure for sample size planning to make inference on Z-scores and percentile rank scores is proposed. Sampling variance formulas fo...
Article
Full-text available
To estimate the mean of a quantitative variable in a hierarchical population, it is logistically convenient to sample in two stages (two-stage sampling), i.e. selecting first clusters, and then individuals from the sampled clusters. Allowing cluster size to vary in the population and to be related to the mean of the outcome variable of interest (in...
Article
Full-text available
In multilevel populations, there are two types of population means of an outcome variable ie, the average of all individual outcomes ignoring cluster membership and the average of cluster‐specific means. To estimate the first mean, individuals can be sampled directly with simple random sampling or with two‐stage sampling (TSS), that is, sampling cl...
Article
Link to 50 free eprints: http://www.tandfonline.com/eprint/DEwnHcycMbABCWzDgdQX/full Fitting cross-classified multilevel models with binary response is challenging. In this setting a promising method is Bayesian inference through Integrated Nested Laplace Approximations (INLA), which performs well in several latent variable models. Therefore we dev...

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