Department’s name

Department of Statistical Modelling

Brief characterization of its research field

Statistics in its various forms, such as e.g. mathematical statistics, biostatistics, applied statistics, econometrics, psychometrics, or even bioinformatics and ‘data science’, represents one of the branches of information sciences which use data to draw inference about the reality. This is specifically achieved using the concept of designed experiments which relates to different fields of research, such as agriculture (e.g. R. A. Fischer’s experiments), biomedical sciences (providing ’big data’ from genomic studies), medicine and pharmacology (e.g. involves statistical methodology for analysing data from clinical trials), engineering, economy (econometrics), social sciences (incl. psychology, pedagogy), and many other fields of scientific research. Observational studies, whether cross-sectional or longitudinal, offer another source of information, the latter rendering time series data. Observational studies may also involve ‘big data’ (e.g. data from satellite worldwide weather monitoring). Consequently, statistical science spans the realm of both basic and applied interdisciplinary research.

Key research trends in the field at hand

Various topics of statistical science are being researched by the members of our department. They include the following: linear and generalised linear models for independent data, mixed variants of the two classes of models for correlated data (e.g. cross-sectional and epidemiological studies), generalised additive models (GAM) allowing for flexible covariate modelling, methods for dimensionality reduction (big data), methods for analysing censored data (longitudinal, cohort studies), missing data imputation, hierarchical regression models, robust methods designed to obtain correct inference in the presence of outliers, change-point problems in time series, etc. In recent years the volume and complexity of data increased dramatically.

Department’s vision

Mathematical statistics is one of the building blocks that is reflected in data management, processing and extraction of useful information from the data while controlling the level of confidence in our conclusions that are often based on large volumes of complex and noisy inputs. The ‘modelling emphasis’ indicates our interest in developing advanced methods and algorithms which take advantage from the design phase of an experiment and lead to optimised decision support in various scientific disciplines mentioned above. The department puts a key emphasis on promoting statistical approaches to solving various problems of basic and interdisciplinary research where the inspiration for developing advanced statistical methodology and algorithms comes primarily from related field of research. Propositions arising from related scientific disciplines are reformulated as statistical hypotheses and new testing procedures are developed to answer the scientific queries.

Substantiation of the vision

The department will contribute to a further development of statistical theory and methodology, primarily in relation to the above mentioned scientific disciplines and principal research topics. Interdisciplinary collaboration with colleagues from social sciences will result in developing new methods for validation of knowledge and psychologic tests, sensitivity analysis for items and tests in relation to different target groups, adaptive testing and mixed regression modelling. New approaches will be developed for testing structural changes in time series and panel data. Department members will continue in biomedical research collaboration, involving e.g. analysis of congenital defects, research of methods and algorithms for the analysis of categorical data (exact and interval estimation), development of advanced statistical methods for the analysis of trends in mortality and its short-term and long-term prediction. Methods will also be developed and refined to use large number of noisy data, often stemming from heterogeneous sources with various prior assumptions. Of special interest will be environmental data where statistical modelling can serve as a common framework for working with huge amounts of data from satellites, climate and meteorological models and various observational networks. Dynamic and/or spatial statistical modelling, of either non- or semi-parametric form, will also be at the centre of our interest. Frequentist as well as Bayesian inference from dynamic and spatial models will be drawn for such data, involving models’ identification and parameter estimation, with applications in e.g. ecology, meteorology and anthropology, medicine and technical disciplines. Dynamic and spatial inference will be applied to e.g. energy consumption modelling. Department’s activities will also include GAM and state-space modelling for vibrodiagnostics, as part of the Czech Academy of Sciences’ programme ‘Strategy 21’.

Selected publications

• Weak properties and robustness of t-Hill estimators
• Score function of distribution and revival of the moment method
• Structural alterations of the language connectome in children with specific language impairment
• Multilevel maximum likelihood estimation with application to covariance matrices
• Association between ambient ozone and health outcomes in Prague
• Major Heat Waves of 2003 and 2006 and Health Outcomes in Prague
• Analysis of meteorological effects on the incidence of tick-borne encephalitis in the Czech Republic over a thirty-year period
• Change point estimation in panel data without boundary issue
• Development and Validation of the Homeostasis Concept Inventory
• The organisation of physiotherapy for people with multiple sclerosis across Europe: a multicentre questionnaire survey
• Why DIF analysis should be a routine part of developing conceptual assessments
• Exploiting Stein’s Paradox in Analysing Sparse Data from Genome-Wide Association Studies
• Dental implants and improvement of oral health-related quality of life
• Methods of molecular biology and bioinformatics
• Sex-Dependent Changes in Striatal Dopamine Transport in Preadolescent Rats Exposed Prenatally and/or Postnatally to Methamphetamine
• Trans-generational neurochemical modulation of methamphetamine in the adult brain of the Wistar rat
• Prediabetes, diabetes and unemployment
• Spatial distribution of bird communities in small forest fragments in central Europe in relation to distance to the forest edge, fragment size and type of forest
• Retrieval of effective cloud field parameters from radiometric data
• Statistical properties of clear and dark duration lengths
• The variability of automated QRS duration measurement
• Structured, physically inspired (gray box) models versus black box modeling for forecasting the output power of photovoltaic plants
• Student-organized street screening for diabetes mellitus and metabolic syndrome: Mere celebration of World Diabetes Day or actual preventive and diagnostic step?
• Increased tissue and circulating levels of dipeptidyl peptidase-IV enzymatic activity in patients with pancreatic ductal adenocarcinoma
• Marginal Models Via GLS: A Convenient Yet Neglected Tool for the Analysis of Correlated Data in the Behavioural Sciences
• Human-sensitive bryophytes retreat into the depth of forest fragments in central European landscape
• Seasonal population dynamics of a specialized termite-eating spider (Araneae: Ammoxenidae) and its prey (Isoptera: Hodotermitidae)
• Reconstruction of effective cloud field geometry from series of sunshine number
• A new point of view on the relationship between global solar irradiation and sunshine quantifiers
• A new perspective on the relationship between cloud shade and point cloudiness
• Professional internet information source used as educational resource for patients with insulin-treated diabetes in the Czech Republic: a 5-year analysis of operations
• Fear of driving license withdrawal in patients with insulin-treated diabetes mellitus negatively influences their decision to report severe hypoglycemic events to physicians
• Value of old forest attributes related to cryptogam species richness in temperate forests: A quantitative assessment
• Active transforming growth factor-β2 in the aqueous humor of posterior polymorphous corneal dystrophy patients

Applications

The results of the research may be find applications in the analysis of complex data in a variety of fields:

• clinical medicine
• biomedical research
• molecular genetics
• epidemiology
• image analysis
• psychometrics
• environmental research

Projects

• Estimation of psychometric measures as part of admission test development
• Effectiveness analysis of prenatal diagnosis of congenital malformations and survival of children born with a birth defect in 1994-2015