arXiv subjects

Andreas Mayr

Publications and source records attributed to Andreas Mayr.

41 records · Page 3Linked to original sources

arXiv2014

The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling

The concept of boosting emerged from the field of machine learning. The basic idea is to boost the accuracy of a weak classifying tool by combining various instances into a more accurate prediction. This general concept was later adapted to the field of statistical modelling. This review article attempts to highlight this evolution of boosting algorithms from machine learning to statistical modelling. We describe the AdaBoost algorithm for classification as well as the two most prominent statistical boosting approaches, gradient boosting and likelihood-based boosting. Although both appraoches are typically treated separately in the literature, they share the same methodological roots and follow the same fundamental concepts. Compared to the initial machine learning algorithms, which must be seen as black-box prediction schemes, statistical boosting result in statistical models which offer a straight-forward interpretation. We highlight the methodological background and present the most common software implementations. Worked out examples and corresponding R code can be found in the Appendix.

stat.ME↗

arXiv2014

Extending Statistical Boosting - An Overview of Recent Methodological Developments

Boosting algorithms to simultaneously estimate and select predictor effects in statistical models have gained substantial interest during the last decade. This review article aims to highlight recent methodological developments regarding boosting algorithms for statistical modelling especially focusing on topics relevant for biomedical research. We suggest a unified framework for gradient boosting and likelihood-based boosting (statistical boosting) which have been addressed strictly separated in the literature up to now. Statistical boosting algorithms have been adapted to carry out unbiased variable selection and automated model choice during the fitting process and can nowadays be applied in almost any possible type of regression setting in combination with a large amount of different types of predictor effects. The methodological developments on statistical boosting during the last ten years can be grouped into three different lines of research: (i) efforts to ensure variable selection leading to sparser models, (ii) developments regarding different types of predictor effects and their selection (model choice), (iii) approaches to extend the statistical boosting framework to new regression settings.

stat.ME↗

arXiv2014

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Generalized additive models for location, scale and shape (GAMLSS) are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from CRAN (http://cran.r-project.org/package=gamboostLSS).

stat.CO↗

arXiv2013

Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are only suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discrimatory power of a prediction rule. Specifically, we propose a component-wise boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures.

stat.AP↗

arXiv2012

Design and Simulation of Molecular Nonvolatile Single-Electron Resistive Switches

We have carried out a preliminary design and simulation of a single-electron resistive switch based on a system of two linear, parallel, electrostatically-coupled molecules: one implementing a single-electron transistor and another serving as a single-electron trap. To verify our design, we have performed a theoretical analysis of this "memristive" device, based on a combination of ab-initio calculations of the electronic structures of the molecules and the general theory of single-electron tunneling in systems with discrete energy spectra. Our results show that such molecular assemblies, with a length below 10 nm and a footprint area of about 5 nm$^2$, may combine sub-second switching times with multi-year retention times and high ($> 10^3$) ON/OFF current ratios, at room temperature. Moreover, Monte Carlo simulations of self-assembled monolayers (SAM) based on such molecular assemblies have shown that such monolayers may also be used as resistive switches, with comparable characteristics and, in addition, be highly tolerant to defects and stray offset charges.

cond-mat.mes-hall↗