Marco Riani

Prof. Marco Riani

University of Parma, Italy
orcid

Research Focus & Metrics

Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2004
Verified
Book
Closed access
AAAnthony C. Atkinson
Anthony C. Atkinson
Marco RianiMarco Riani
Marco Riani
ACAndrea Cerioli
Andrea Cerioli

22 December 2008 in Journal of the Royal Statistical Society Series B: Statistical Methodology
Verified
Journal Article
Closed access
PDF file
Marco RianiMarco Riani
Marco Riani
ANTHONY ATKINSONANTHONY ATKINSON
ANTHONY ATKINSON
ACAndrea Cerioli
Andrea Cerioli

1 July 2012 in Chemometrics and Intelligent Laboratory Systems
Verified
Journal Article
Closed access
Marco RianiMarco Riani
Marco Riani
DPDomenico Perrotta
Domenico Perrotta
FTFrancesca Torti
Francesca Torti

1 June 2010 in Journal of the Korean Statistical Society
Verified
Journal Article
Closed access
AAAnthony C. Atkinson
Anthony C. Atkinson
Marco RianiMarco Riani
Marco Riani
ACAndrea Cerioli
Andrea Cerioli

1 September 1998 in Computational Statistics & Data Analysis
Verified
Journal Article
Closed access
SZSergio Zani
Sergio Zani
Marco RianiMarco Riani
Marco Riani
ACAldo Corbellini
Aldo Corbellini

8 June 2022 in Statistical Methods & Applications
Verified
Journal Article
Open access
PDF file
Marco RianiMarco Riani
Marco Riani
ANTHONY ATKINSONANTHONY ATKINSON
ANTHONY ATKINSON
ACAldo Corbellini
Aldo Corbellini
The paper introduces an automatic procedure for the parametric transformation of the response in regression models to approximate normality. We consider the Box–Cox transformation and its generalization to the extended Yeo–Johnson transformation which allows for both positive and negative responses. A simulation study illuminates the superior comparative properties of our automatic procedure for the Box–Cox transformation. The usefulness of our procedure is demonstrated on four sets of data, two including negative observations. An important theoretical development is an extension of the Bayesian Information Criterion (BIC) to the comparison of models following the deletion of observations, the number deleted here depending on the transformation parameter.