Davide Ferrari,
Professor of Statistics
Contact
University of Bolzano
Piazza Università 1 39100,
Bolzano, Italy
Phone: +39 0471013307
E-mail: davferrari@unibz.it
About me
I am an associate professor of Statistics in the Faculty of Economics and Management at the University of Bolzano in Italy. I am also an associate research fellow at the Australian Research Council Center of Excellence for Mathematical and Statistical Frontiers (ACEMS). At the University of Bolzano I teach theoretical and applied statistics at both undergraduate and graduate levels. I am currently the coordinator of exchange research and teaching agreements with partner universities for the Faculty of Economics.
My research interests include data integration, composite likelihood procedures, model selection, and inference methods for high-dimensional data. Recently, I have started working on model selection and estimation methods for intractable likelihoods in the context of complex econometric and environmental data which allows me to combine my interest in likelihood-based methods with my interest in applications.
ACADEMIC POSITIONS
EDUCATION
2003 - 2008
PhD in Statistics, University of Minnesota
2003 - 2005
MSc in Statistics, University of Minnesota
1998 - 2002
Laurea in Economics, University of Modena
2016 - 2018
Senior Lecturer (US Assoc. Prof), School of Mathematics and Statistics, University of Melbourne
2012 - 2016
Lecturer (US Assist. Prof), School of Mathematics and Statistics, University of Melbourne
2009 - 2011
Ricercatore (US Assist. Prof.), University of Modena and Reggio Emilia
2018 - Present
Associate Professor, Faculty of Economics and Management, University of Bolzano
MY LATEST TEACHING
Applied Statistics for Accounting and Finance 25408 · SECS-S/01 · Master Accounting and Finance (LM-77) · EN
Methods for business analysis 27174 · SECS-S/01 · Master Entrepreneurship and Innovation (LM-77) · EN
Statistics 27010 · SECS-S/01 · Economics and Management (L-18) · IT
RECENT RESEARCH
Huang, Z. and Ferrari, D, Fast construction of efficient estimating equations. The increasing size and complexity of modern data challenges the applicability of traditional likelihood-based inference. Composite likelihood (CL) methods address the difficulties related to model selection and computational intractability of the full likelihood by combining a number of low-dimensional likelihood objects into a single objective function used for inference. We introduce a procedure to combine partial likelihood objects from a large set of feasible candidates and simultaneously carry out parameter estimation. The new method constructs estimating equations balancing statistical efficiency and computing cost by minimizing an approximate distance from the full likelihood score subject to a L1-norm penalty representing the available computing resources. An asymptotic theory within a framework where both sample size and data dimension grow is developed and finite-sample properties are illustrated through numerical examples.
Zheng, C, Yang, Y. and Ferrari, D. Model selection confidence sets by likelihood ratio testing. The traditional activity of model selection aims at discovering a single model superior to other candidate models. In the presence of pronounced noise, however, multiple models are often found to explain the same data equally well. To resolve this model selection ambiguity, we introduce the general approach of model selection confidence sets (MSCSs) based on likelihood ratio testing. A MSCS is defined as a list of models statistically indistinguishable from the true model at a user-specified level of confidence, which extends the familiar notion of confidence intervals to the model-selection framework. Our approach guarantees asymptotically correct coverage probability of the true model when both sample size and model dimension increase. We derive conditions under which the MSCS contains all the relevant information about the true model structure. In addition, we propose natural statistics based on the MSCS to measure importance of variables in a principled way that accounts for the overall model uncertainty.
Illustration of the trade-off between statistical and computational efficiency for a m-variate normal random vector X. Top row: Sub-likelihood weights for increasing penalty values (lambda) with corresponding number of sub-likelihoods reported at the top. Bottom row: Asymptotic relative efficiency of the implied CL estimator compared to maximum likelihood. The columns correspond to dimension (m) and pairwise correlation (\rho) between elements in X.
Huang, Z., Qian, G. and Ferrari, D., Parsimonious and powerful composite likelihood testing for group difference and genotype-phenotype association. Testing the association between a phenotype and many genetic variants from case-control data is essential in genome-wide association study (GWAS). This is a challenging task as many such variants are correlated or non-informative. Similarities exist in testing the population difference between two groups of high dimensional data with intractable full likelihood function. Testing may be tackled by a maximum composite likelihood (MCL) not entailing the full likelihood, but current MCL tests are subject to power loss for involving non-informative or redundant sub-likelihoods. In this paper, we develop a forward search and test method for simultaneous powerful group difference testing and informative sub-likelihoods composition. Our method constructs a sequence of Wald-type test statistics by including only informative sub-likelihoods progressively so as to improve the test power under local sparsity alternatives. Numerical studies show that it achieves considerable improvement over the available tests as the modeling complexity grows. Our method is further validated by testing the motivating GWAS data on breast cancer with interesting results obtained.