# Keynote Speakers

Prof. João
Tiago Praça Nunes Mexia

New University of Lisbon,
Portugal

João Tiago Praça Nunes Mexia was born in Lisbon in June of 1939. The most part of his career was as Full Professor at the FCT/UNL-Faculty for Sciences and Technology of the New University of Lisbon. At that time he supervised the teaching of Statistics at FCT/UNL and directed the Research Center in Mathematics of the University (CMA-Center for Mathematics and its Applications) from 1999 to 2009. In 2009 he became Emeritus Professor. Until now he supervised 19 Ph.D. and co-supervised 12 Ph.D. His research is centered on Linear Statistical Inference, having almost 100 papers published in International Journals.

Speech Title: "Confidence Ellipsoids for Additive Pearsonian Models"

Abstract:

Prof. Carlos
A. Braumann

University of Évora, Portugal

Carlos A. Braumann is Emeritus Professor and member of the research centre CIMA at the University of Évora (UE), Portugal, where he has been Vice-Rector in 1987-94 and Rector in 2010-14. His publications are mostly on Stochastic Differential Equations and its applications in several areas (population dynamics, fisheries, animal growth, demography, finance). He got his Ph.D. in 1979 at the Stony Brook University and his habilitation in Stochastic Processes at the UE in 1988. He is an elected member of the International Statistical Institute since 1992, a former President of the European Society for Mathematical and Theoretical Biology (2009-12) and of the Portuguese Statistical Society (2006-09 and 2009-12), and a former member of the European Regional Committee of the Bernoulli Society (2008-12).

Speech
Title: "Individual
Growth Modelling with
Stochastic Differential
Equations"

Authors: Carlos A. Braumann,
Patrícia A. Filipe, and
Gonçalo Jacinto

Abstract: Common growth
curves for the weight X(t)
of an animal at age t can be
described by a differential
equation of the form
dY(t)=β(α-Y(t))dt, where
Y(t)=h(X(t)) and h is an
appropriate strictly
increasing continuously
differentiable function,
α=h(A) (A= maturity weight
of the animal), and β>0 is a
rate of approach to
maturity. Adjustment to data
was usually done through
non-linear regression
inappropriate methodology
that ignores the growth
dynamics and the influence
of environmental
fluctuations on it. Instead,
we use instead stochastic
differential equations
(SDEs) models
dY(t)=β(α-Y(t))dt+σdW(t),
where W(t) is a standard
Wiener process and σ is an
intensity parameter of the
fluctuations. We have
previously studied
estimation, prediction and
optimization issues using
cattle weight data from
females of Mertolengo cattle
breed. In the present work,
we have adjusted and applied
the methodologies to the
weight data of males of
Mertolengo cattle breed and
Alentejana cattle breed.
Since model parameters may
vary from animal to animal
and that variability can be
partially explained by their
genetic differences, we
introduce the extension of
the study to SDE mixed
models. These mixed models
incorporate the individual
genetic values that are
available at the databases
of the producer
associations.

Acknowledgements: The
authors belong to the
research centre Centro de
Investigação em Matemática e
Aplicações (CIMA),
Universidade de Évora,
supported by FCT (Fundação
para a Ciência e a
Tecnologia, Portugal,
project UID/MAT/04674/2019).

# Invited Speakers

Prof.
Alexander Bulinski

Moscow State University, Russia

Alexander Bulinski, Professor of the Moscow State University, Dr. Sc. Phys. Math. (Habilitation) is a Member of the Board of the Moscow Mathematical Society since 2000, was a Member of the European Committee of the Bernoulli Society (2002-2006). He is an author of 5 books and numerous research papers. His main results pertain to the theory of stochastic processes and random fields. Various statistical applications of limit theorems are also in the scope of his activity. A.Bulinski belongs to the scientific school of Professor A.N.Kolmogorov being his former PhD student. He was awarded the State Scholarship for prominent scientists and International Science Foundation Diploma ``for outstanding contribution to world science and education''. He is a winner of the Lomonosov prize in Science. A.Bulinski is a Member of the Editorial Boards of 6 journals. He was Invited Professor in France, Germany, Sweden, Netherlands, UK etc. Under his scientific direction 15 PhD-theses were written and 4 are in preparation. He was Keynote Speaker and Invited Speaker, as well as a member of Program Committees, at various International conferences. A.Bulinski is a Member of the Expert Council for Higher Qualification Committee of Russia, Head of the Federal Teaching Union on Mathematics and Mechanics in the Higher Education System of Russia.

Speech Title: "Statistical Estimation of Mutual Information and Applications"

Abstract: Statistical estimation of mutual information is important for various applications. Such estimates are employed, for instance, in machine learning, feature selection and identification of textures inhomogeneities. In this regard one can refer, e.g., to the book by V.Bolon-Canedo and A.Alonso-Betanzos (2018), see also a review by J.R.Vergara and P.A.Estevez (2014). We develop the quite recent papers by A.Bulinski, A.Dimitrov (2018) and A.Bulinski, A.Kozhevin (2018), concerning the Shannon entropy, to study statistical estimation of mutual information and the Kullback-Leibler divergence. We investigate the asymptotic properties of proposed estimates constructed by means of i.i.d. (vector-valued) observations. For this purpose we apply the techniques involving the nearest neighbor statistics. Special attention is payed to results of computer simulations in the framework of mixed models (see, e.g. F.Coelho, A.P.Braga, M.Verleysen (2016), W.Gao, S.Kannan, P.Viswanath (2018)) comprising the widely used logistic regression. In contrast to previous works we do not suppose that the set of a response variable values is endowed with nontrivial metric. This is essential in many cases for analysis of medical and biological data.

Prof. Xingbo
Wang

Foshan University, China

Dr. & Prof. Xingbo Wang got his Master and Doctor’s degrees at National University of Defense Technology (NUDT) of China. Since 1994, he had worked at NUDT on CAD/CAM/CNC technologies till 2010. Since 2010, he has been a professor in Foshan University with research interests in intelligent manufacturing system and computer applications. Prof. Wang is now in charge of Guangdong Engineering Center of Information Security for Intelligent Manufacturing System, where a lot of cryptography problems have to be dealt with the elementary number theory. He then set up a new method to study odd integers by means of perfect full binary tree and derived out many new properties of the odd integers, including genetic property that makes it easier to factorize an odd integer. Now Prof. Wang is devoting himself to developing a fast algorithm to integer factorization and intending to solve the hard problem of integer factorization.

Speech Title: "Deterministic-embedded Monte Carlo Approach to Find out an Objective Item in a Large Number of Data Sets"

Abstract: The paper investigates an approach to find out an objective integer in a large integer interval. It first puts forward an approach to subdivide a large integer interval into small ones that are available for the Monte Carlo randomized search algorithm, then selects a small interval by the Monte Carlo algorithm and applies a deterministic search algorithm on the selected one. In order to make the search in an expected computing time, the paper proposes certain regulations to set an initial length for the small interval and to update it in accordance with the expectation of the time complexity. Mathematical foundations for setting-up the initial value and updating it to an acceptable value are presented and proved in detail and a parallel computing strategy is introduced to realized it. Except for the availability in integer factorization, the approach is also applicable in big data searches.