# New Developments in Statistics

## Subject description

Contents will be selected among the following topics:

• Mathematical statistics.

• Bayesian methods in statistics.

• Simulation methods for statistical research.

• Point processess.

• Time series.

• Multivariate analysis.

• Analysis of nominal data.

• Statistical modelling.

• Nonparametric statistics.

• Research design and data collection.

• Measurement and data collection in official statistics.

• Survey methodology.

• Missing data.

• Network analysis.

• Event history analysis.

• Methods for analysing high-dimensional data.

• Design and analysis of experiements.

• Psychometrics.

• Data mining methods.

• Statistical process control.

• Specific statistical approaches and methods in biology, social sciences, economics and management, medicine, psychology, engineering and other sciences.

## Objectives and competences

The aim of the course is to give an overview of some of the most contemporary fields of statistics, among which the students could choose their doctoral theses topics. The lectures will be given by local and foreign experts in each selected field. The course will include mandatory provision of consulting for users of statistical methods.

## Teaching and learning methods

• Lectures.

• Advanced essay submission and presentation as part of the course.

• Consulting to users of statistical methods.

## Expected study results

Knowledge and understanding: Knowledge of modern statistical approaches, the ability to use and evaluate their results. Application: Use in solving statistical problems.

Reflection: Getting to know the latest statistical techniques and an understanding of how these methods are used to solve specific research problems.

## Basic sources and literature

Dobršen del virov se bo prilagajal trenutnemu izboru tem, našteti so le nekateri trenutno aktualni viri / A large part of the literature will be adopted to the current selection of topics; some of the currently recommended textbooks are listed below:

• Gelman, J.B.Carlin, H.S. Stern, D.B. Rubin: Bayesian Data Analysis. Chapman&Hall, 1995.

• P. J. Brockwell, R. A. Davis: Time series: Theory and methods, 2006.

• S. I. Resnick: Adventures in stochastic processes, 1992.

• M. Verbeek: A guide to modern econometrics, 2004.

• O. Aalen, O. Borgan, H. Gjessing: Event History Analysis: A Process Point of View. Springer-Verlag, 2008.

• Skrondal, A. in Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.

• McDonald, R.P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum Associates.

• R.S. Kenett, S.Zacks: Modern Industrial Statistics, Duxbury Press, 1998

• P. Doreian, V. Batagelj, A. Ferligoj. Generalized Blockmodeling. Cambridge University Press, 2005.

• P. J. Carrington, J. Scott, S. Wasserman (ur.): Models and Methods in Social Network Analysis. Cambridge University Press, 2005.

• K.V. Mardia, J.T. Kent in J.M. Bibby: Multivariate analysis. Academic Press, London, 1989.

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