purpose the aims of the present study were to investigate (i) the changes in participation and performance and (ii) the gender difference in triple iron ultra-triathlon (11.4 km swimming, 540 km cycling and 126.6 km running) across years from 1988 to 2011. methods for the cross-sectional data analysis, the association between with overall race times and split times was investigated using simple...

A common study to investigate gene-environment interaction is designed to be longitudinal and population-based. Data arising from longitudinal association studies often contain missing responses. Naive analysis without taking missingness into account may produce invalid inference, especially when the missing data mechanism depends on the response process. To address this issue in the ana...

The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of us...

MOTIVATION Metabolomics datasets are generally large and complex. Using principal component analysis (PCA), a simplified view of the variation in the data is obtained. The PCA model can be interpreted and the processes underlying the variation in the data can be analysed. In metabolomics, often a priori information is present about the data. Various forms of this information can be used in an u...

SUMMARY Functional data analysis provides an inherently nonparametric approach for the analysis of data which consist of samples of time courses or random trajectories. It is a relatively young field aiming at modeling and data exploration under very flexible model assumptions with no or few parametric components. Basic tools of functional data analysis are smoothing, functional principal compo...

Inverse regression, or statistical calibration, uses the estimated relationship between a response Y and a covariate x to infer the values of unknown x’s from their observed Y’s. Typically x is univariate but Y may be multivariate. A brief review of the basic theory will be given, followed by consideration of the problems involved in extending these approaches to longitudinal data, i.e. where t...