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Keynote Speaker IIASA Fellow, Prof. Ding-Geng ChenUniversity of North Carolina-Chapel Hill, USADr. (Din) Ding-Geng Chen is the Wallace H. Kuralt distinguished professor at the School of SocialWork and a professor in biostatistics at the Department of Biostatistics at Gillings School of GlobalPublic Health from the University of North Carolina-Chapel Hill, USA. Before this, he was a professorin biostatistics at the University of Rochester Medical Center, the Karl E. Peace endowed eminentscholar chair and professor in biostatistics from the Jiann-Ping Hsu College of Public Health at theGeorgia Southern University. Dr. Chen is an elected fellow of American Statistical Association (ASA),an elected member of the International Statistics Institute (ISI) and a senior expert consultant forbiopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. Hehas more than 200 professional publications and co-authored/co-edited 30 books on biostatistics clinicaltrials, biopharmaceutical statistics, interval-censored survival data analysis, meta-analysis, public healthstatistics, statistical causal inferences, statistical methods in big-data sciences and Monte-Carlosimulation based statistical modeling. He has been invited internationally to speak and give shortcourses and tutorials at various scientific conferences.Speech Title: "Data Fusion and Statistical Meta-Analysis in Big-Data Era"Abstract—Data fusion and statistical meta-analysis is a process of integrating multiple data sources anddiverse studies to produce more reliable and efficient conclusion. In this big-data era, it is natural, ratherthan the exception, that the data collected to address the same/similar scientific question come fromdiverse sources. The art and science of synthesizing information from diverse sources to draw a moreeffective inference is generally referred to as systematic reviews and meta-analysis It can be performedby either synthesizing study-level summary statistics (SS) or modeling individual participant-level data(IPD), if available. However, it remains not fully understood whether the use of IPD indeed gainsadditional efficiency over SS. In this talk, we discuss the relative efficiency of the two methods under ageneral likelihood inference setting. We show theoretically that there is no gain of efficiencyasymptotically by analyzing IPD, provided that the random-effects follow the Gaussian distribution andmaximum likelihood estimation is used to obtain summary statistics. Our findings are confirmed bysimulation studies and a real data analysis of beta-blocker treatment effect for myocardial infarction.This is a joint work among Drs. Dungang Liu, Xiaoyi Min and Heping Zhang, paper published atBiometrics (Chen, D.G, Liu, D., Min, X. and Zhang H. Relative efficiency of using summary /biom.13238).11

Keynote Speaker IIIProf. Alexander BulinskiMoscow State University, RussiaAlexander Bulinski, Professor of the Moscow State University, Dr. Sc. Phys. Math. (Habilitation) is aMember of the Board of the Moscow Mathematical Society since 2000, was a Member of theEuropean Committee of the Bernoulli Society (2002-2006). He is an author of 5 books and numerousresearch 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.Bulinskibelongs to the scientific school of Professor A.N.Kolmogorov being his former PhD student. He wasawarded 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 Lomonosovprize in Science. A.Bulinski is a Member of the Editorial Boards of 6 journals. He was InvitedProfessor in France, Germany, Sweden, Netherlands, UK etc. Under his scientific direction 15PhD-theses were written and 4 are in preparation. He was Keynote Speaker and Invited Speaker, aswell as a member of Program Committees, at various International conferences. A.Bulinski is aMember of the Expert Council for Higher Qualification Committee of Russia, Head of the FederalTeaching Union on Mathematics and Mechanics in the Higher Education System of Russia.Speech Title: "Feature Selection Theory and Applications"Abstract—The talk is devoted to study of stochastic models described by means of some featurescollection X (X1; : : : ;Xn) and a response variable Y: Such models are widely used, e.g.,inmedicine and biology, where Y can characterize the health state of a patient and X comprisesgenetic and nongenetic factors. We are interested in models involving high-dimensional observations.A challenging problem is to identify the sub-collection of in uential (in a sense) factors. There arewell-known feature selection procedures. Here we mention only few of them: Booleanoperation-based screening and testing (BOOST), least absolute shrinkage and selection method(LASSO), adaptive and group LASSO, nonnegative (NN) Garrote, penalized regression withsmoothly clipped absolute deviation (SCAD) penalty, least angle regression (LAR) and generalizedDantzig selector (DS). Traditionally feature selection methods are classi ed into lters, wrappers andembedded ones. The hybrid methods are also treated. An important subject is the development ofalgorithms for structured features (e.g., having group or tree, or graph structure). Usually oneassumes that all features are known in advance. However, in certain models the candidate features aregenerated dynamically, thus the size of features is unknown. Such features are named streaming onesand a streaming feature selection arises. Feature selection methods give a possibility to improve theprediction performance, to reduce the computation time and better understand the structure of thedata. This research domain is located at the border of the modern statistics and machine learning.Along with a survey we concentrate on the new results involving various concepts of informationtheory. Among tools we mention the transfer entropy and the Kull-back - Leibler divergence.Appropriate theorems are provided to describe the asymptotic properties of statistics underconsideration. Cross-validation and stability problems (including the choice of di erent stabilitymeasures) are in the scope as well. Special attention is paid to applications in bioinformatics, namely,in genome-wide association studies (GWAS). We tackle also the simulation problems related tofeature selection.12

Invited Speaker IProf. João Tiago Praça Nunes MexiaUniversidade Nova de Lisboa, PortugalProf. João Tiago Praça Nunes Mexia was born in Lisbon in June of 1939. The most part of his careerwas as Full Professor at the FCT/UNL-Faculty for Sciences and Technology of the New Universityof Lisbon. At that time he supervised the teaching of Statistics at FCT/UNL and directed theResearch Center in Mathematics of the University (CMA-Center for Mathematics and itsApplications) from 1999 to 2009. In 2009 he became Emeritus Professor. Until now he supervised 19Ph.D. and co-supervised 12 Ph.D. His research is centered on Linear Statistical Inference, havingalmost 100 papers published in International Journals.Topic: “Additive Models: Estimation, Prediction and Structured Families”Abstract—We generalize the classic Scheffe Theorem, that will be applied to normal vectors withcovariance matrix δ2C; known up to δ2; independent from S; being S the product by δ2 of a centralchi-square. Namely, C may be not invertible. We obtain confidence ellipsoids and simultaneousconfidence in-tervals, through duality. These confidence intervals will be used to "refine" F tests forfixed effects models. The asymptotic version of these results will be obtained using the continuousmapping theorem. Lastly we consider the case of the singular multinomial distribution.13

Invited Speaker IIAssoc. Prof. Nazrina AzizUniversiti Utara Malaysia, MalaysiaNazrina Aziz is currently Associate Professor of Statistics in the Department of Mathematics andStatistics at the Universiti Utara Malaysia. She has taught a vast range of statistics courses forundergraduate degrees, master’s degrees and training courses. Her research interest concerns theinvestigation of theoretical problems and applications in a variety of areas such as AcceptanceSampling, Satisfaction Survey, Program Evaluation Survey, Disease Mapping and StatisticalModeling in Healthcare. She has involved in developing a few instruments to measure industrialemployees’ and tertiary education’s satisfaction. She has also edited three books, four scopus indexedproceedings and contributed five chapters in different books. Currently, a majority of herpostgraduate students are investigating on acceptance sampling. Most of their works have beenpublished in indexing journals and proceedings.Title: New Group Chain Sampling Plan (NGChSP-1) for Generalized Exponential DistributionAbstract—Recently, many researchers have developed various acceptance sampling plans to be usedby producers in making decision whether to accept or reject inspected lots. In 2015, Mughal, Zainand Aziz proposed group chain sampling plan (GChSP) that solves the issues in chain acceptancesampling plans (ChSP) (time complexity during inspection) and group acceptance sampling plans(GSP) (insecure probability of lot acceptance). GChSP was created based on three acceptance criteria,the first being no defective is recorded in the current lot - without considering the number ofdefectives found in preceding lot. This contradicts with the condition of having a cumulative of onedefective for the current lot to be accepted. Hence, in this article we developed the new group chainsampling plan (NGChSP-1) to handle the issue of acceptance criteria in GChSP. The NGChSP-1 isdeveloped based on truncated life test for generalized exponential distribution. The performancecomparison of the NGChSP-1 and GChSP are discussed in the findings.14

Detailed Program for Oral SessionSession 1: Pure MathematicsTime: 10:00-11:40, June 23, 2020 (Tuesday)Greenwich Mean Time (GMT 02:00) – France Local TimeMeeting ID: 639-9428-1834Session Chair: Dr. Sandra Ferreira, New University of Lisbon, istence of the Solution for the Problem in Subdi usive Medium with aMoving Concentrated SourceHon-hung Terence LiuTatung University, TaiwanAbstract—This paper investigates the problem of a fractional differentialequation with moving concentrated source in an infinite rod. Thisfractional differential operator is used to formulate the diffusion problemin a subdiffusive medium. The existence of the solution is established, thefinite time blow-up criteria for the solution of the problem is given. Thecritical speed of the concentrated sources on the behavior of the solutionare investigated. keywords. Green's function, fractional diffusionequations, fractional derivatives, moving concentrated source AMS(MOS) Subject Classification. 35R11, 35R12.Metrics for Multi-Set-Theoretic Graphs over Fixed Set of VerticesRay-Ming ChenBaise University, ChinaAbstract—In this article, we show how to define distance functions formultiedged graphs, either with vertices being labelled or unlabelled.These metrics are constructed mainly based on the minimal matching formulti-sets. In the end, we implement such metrics based on adjacentmatrices. These metrics provide some important applications in the A Note on Integrating Factors of a Conformable Fractional DifferentialEquationF. Martínez, I. Martínez and S. ParedesUniversidad Politécnica de Cartagena, SpainAbstract—Recently exact fractional differential equations have beenintroduced, using the conformable fractional derivative. In this paper, wepropose and prove some new results on the integrating factor. Weintroduce a conformable version of several classical special cases forwhich the integrating factor can be determined. Specifically, the cases wewill consider are where there is an integrating factor that is a function ofonly x, or a function of only y, or a simple formula ofand . Inaddition, using the Conformable Euler's Theorem on homogeneousfunctions, an integration factor for the conformable homogeneousdifferential equations is established. Finally, the above results apply insome interesting examples.A Knot Selection Algorithm for Splines in Logistic RegressionTzee-Ming HuangNational Chengchi University, Taiwan15

Detailed Program for Oral SessionS1-5F201111:20-11:40Abstract—In ordinary logistic regression, the logit of the conditionalprobability of the response given the covariates is modeled as a linearfunction of the covariates. In this study, a more general logistic regressionmodel is considered, where linearity is not assumed. Since the linearfunction of the covariates is replaced by a general function of thecovariates, spline approximation is used. A knot selection algorithm isproposed to determine the knot locations in spline approximation.Simulation experiments have been carried out to check the performanceof the proposed algorithm. The proposed algorithm performs reasonablywell.On the Construction of Multivariate correlation CoefficientsJochen Merker and Gregor SchuldtLeipzig University of Applied Sciences, GermanyAbstract—A multivariate (un)correlation coefficient maps a vector-valuedrandom variableto a real number between 0 and 1,which indicates how linearly (un)correlated its componentsare. In this paper, we provide a unified framework formultivariate (un)correlation coefficients known in literature, and constructnew multivariate (un)correlation coefficients using Rényi entropies,which allow applications in many scientific areas.16

Detailed Program for Oral SessionSession 2: Applied StatisticsTime: 10:00-11:40, June 23, 2020 (Tuesday)Greenwich Mean Time (GMT 02:00) – France Local TimeMeeting ID: 940-2336-5584Session Chair: Assoc. Prof. Nazrina Aziz, Universiti Utara Malaysia, 0:40F000510:40-11:00Auxiliary Information for Improving the Statistical Matching of Incomeand ConsumptionIrene Rioboo and Friderike OehlerEuropean Commission, LuxembourgAbstract—This exercise addresses the joint distribution of income andconsumption produced through a statistical matching of the EuropeanStatistics on Income and Living Conditions (EU-SILC) and theHousehold Budget Survey (HBS). The major limitation of the methodsgenerally used in statistical matching is that they are based on theConditional Independence Assumption (CIA), which is very strict, seldomholds and is difficult to test with the data available. In order to relax oravoid this assumption we test two alternatives. The first option uses asmatching variable a proxy of the EU-SILC target variable. The highcorrelation between those variables allows considering the CIA aplausible hypothesis. The second option avoids the CIA by using a thirdsource where income and consumption are jointly observed: the moduleon Over-indebtedness, Consumption and Wealth (OWC) of the 2017EU-SILC wave. This exercise compares both options and their role inimproving the estimates provided by the statistical matching.Estimating of Factors Influencing the Brewing Market by Using DecisionTrees: A Case of BulgariaHristina Kulina, Snezhana Gocheva-Ilieva and Atanas IvanovUniversity of Plovdiv Paisii Hilendarski, BulgariaAbstract—This study examines the influence of key market factors –price, distribution, digital and non-digital advertising, atmospherictemperature and others on the sales of the brewing sector in Bulgaria. Themonthly observations over nearly five years are analyzed for a majorbrand of beer. The data are modeled using the powerful data miningtechnique of Classification and Regression Trees (CART). The builtmodels describe beer sales in relation to the studied factors with highgoodness-of-fit statistics: coefficient of determination up to R2 94% andRMSE 3.11. Cross-validation and holdout data sampling are used toassess the quality of obtained models. The models are applied forforecasting the volume of beer sales for one month ahead.Assessing the Risk of Cancer Prevalence from Lifestyle Activities amongStudents I n Bloemfontein, South AfricaG. Makanda, R. Sypkens and M.S MakandaCentral University of Technology, South AfricaAbstract—The prevalence of cancer and cardiovascular diseases hascaused one of the highest death rates in the world. Research has shown17

Detailed Program for Oral t lifestyle is the major cause of cancers. Cancerous cells develop duewhat people eat and how they live their lives. This research investigatesthe risk of cancer occurrence in Bloemfontein in South Africa. Theresearch was carried out by using a questionnaire developed by CANSASouth Africa on this assessment. The questionnaire included variouslifestyle questions. The data was then analyzed using SPSS and EvaSys(Automated Evaluation Software). The results show that most people donot eat healthy and thereby having a high risk of developing cancers.Machine Learning Versus Statistical Methods in Demand Planning forEnergy-efficient Supply ChainsLucas Schreiber and Nikolas MoroffFraunhofer Institute for Material Flow and Logistics, GermanyAbstract—The research project "E²-Design" intends to integrate energyefficiency as a planning parameter in the design of production andlogistics networks. In order to achieve this objective, models and methodsare developed that enable an appropriate approach in the strategic andtactical planning of supply chains. It was observed that models andmethods for designing supply chains are always based on accuratedemand forecasting. This is equally true for the design of supply chainswith the core objective of energy efficiency. The better and more granularthe demand forecast can be performed, the more valid recommendationsfor action can be provided to improve energy efficiency. To accomplishthis, this paper aims at identifying promising models for the selection andimplementation of a demand forecasting algorithm. By an initialcomparison of statistical methods with machine learning methods, highpotentials in the context of machine learning will be identified.Subsequently, several process models for implementing a suitablemachine learning algorithm

ICoMS 2020 is organized to bring together worldwide leading researchers and practitioners interested in advancing the state of the art in Mathematics and Statistics, for exchanging knowledge . the stationary distribution based on the evolution of the semi-Markov system in a time interval. Basic properties and estimation problems of stationary .