In this contribution, the basic overview of the Object Oriented Programming and its usage in computation is given. Concepts of class, encapsulation, inheritance, and polymorphism are introduced. Some additional concepts like interface and Design Patterns are briefly discussed. Schematic examples...

The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood (ML) estimates, useful in a variety of incomplete-data problems. Maximum likelihood estimation and likelihood-based inference are of central importance in statistical...

Ensemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The general principleof ensemble methods is to construct a linear combinationof some model fitting methods, instead of using a single fit of the method.

Since the last decade we live in a digitalized world where many actions in human and economic life are monitored. This produces a continuous stream of new, rich and high quality data in the form of panels, repeated cross-sections and long time series . These data resources are available to many...

The theory of embedded markets states that economic transactions are embedded in social relationships, and economic actors are influenced by both marketing variables and relationship properties. That is, within an exchange relationship actors derive utility from the attributes of a focal product...

In the past fifteen years computational statistics has been enriched by a powerful, somewhat abstract method of generating variates from a target probability distribution that is based on Markov chains whose stationary distribution is the probability distribution of interest. This class of...

Parallel computing means to divide a job into several tasks and use more than one processor simultaneously to perform these tasks. Assume you have developed a new estimation method for the parameters of a complicated statistical model. After you prove the asymptotic characteristics of the method...

We study the modelling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalising the volatility in high...

Statistische Prognosen basieren auf der Annahme, dass ein funktionaler Zusammenhang zwischen der zu prognostizierenden Variable y und anderen dimensionalen beobachtbaren Variablen x=(x1,...,xj)t – Rj besteht. Kann der funktionale Zusammenhang geschätzt werden, so kann im Prinzip für jedes x...

Tree-based methods have become one of the most flexible, intuitive, and powerful data analytic tools for exploring complex data structures. The applicationsof these methods are far reaching. They include financial firms (credit cards: Altman, 2002; Frydman et al., 2002, and investments: Pace, 1995;...