Functional variance estimation using penalized splines with principal component analysis

In many fields of empirical research one is faced with observations arising from a functional process. If so, classical multivariate methods are often not feasible or appropriate to explore the data at hand and functional data analysis is prevailing. In this paper we present a method for joint modeling of mean and variance in longitudinal data using penalized splines. Unlike previous approaches we model both components simultaneously via rich spline bases. Estimation as well as smoothing parameter selection is carried out using a mixed model framework. The resulting smooth covariance structures are then used to perform principal component analysis. We illustrate our approach by several simulations and an application to financial interest data.     

Innere Bilder und beruflicher Lebensweg

This article gives an introduction into working with inner images. In the background of professional work and career there are inner images at work. They trigger which roles we take and which plays on which stages we choose or accept as meaningful. A Case study elaborately illustrates working with inner background images. Simple questions lead to relevant images, which influence professional life. A guide for interviewing and practical approaches can be found in the supplement.     

Tagungsberichte

Tagungsberichte

Tagungsberichte     

Runtime and memory consumption analyses for machine learning R programs

R is a multi-paradigm language with a dynamic type system, different object systems and functional characteristics. These characteristics support the development of statistical algorithms at a high level of abstraction. Although R is commonly used in the statistics domain a big disadvantage are its runtime problems when handling computation-intensive algorithms. Especially in the domain of machine learning the execution of pure R programs is often unacceptably slow. Our long-term goal is to resolve these issues and in this contribution we used the traceR tool to analyse the bottlenecks arising in this domain. Here we measured the runtime and overall memory consumption on a well-defined set of classical machine learning applications and gained detailed insights into the performance issues of these programs.