Part II is based upon three papers that were presented in the Special Session Information Theory and Statistical Applications of the 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007) which was held in May 2007 in Chania, Greece. Information theory includes research dealing, among others with statistical inference, association, prediction, and modelling of statistical data. The last two or three decades are characterised by a vigorous growth in the use of information-theoretic ideas and methods in statistics. The reason is that Statistical Information Theory (SIT) provides a number of measures which obey nice probabilistic and statistical properties and moreover can be used to formulate and solve a great variety of statistical problems. In this sense SIT contributes to the advancement in probability theory and statistics in particular, and progress in almost all areas of science and engineering.
KeywordsDivergence Measure Statistical Inference Model Selection Criterion Vigorous Growth Special Session
- Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Proc. of the 2nd Intern. Symposium on Information Theory, (Petrov B. N. and Csaki F., Eds.), Akademiai Kaido, Budapest.Google Scholar
- Vajda, I. (1989). Theory of Statistical Inference and Information. Kluwer Academic, Dordrecht.Google Scholar