arXiv subjects

Andreas Groll

Publications and source records attributed to Andreas Groll.

At least 19 recordsLinked to original sources

arXiv2026

Text Data Analysis and Classification Methods - Insights from Customer Letters in Life Insurance

The business of life insurance companies is characterized by long-term contracts. For this reason, data describing customers is of immense value. A portion of the data provided to the customer is rarely or not at all analyzed. This includes customer letters of any kind. This work focuses on classifying customer letters as cancellations and identifying the respective reason, if available. The outlined approach can also be applied to other business transactions and reasons. We discuss data acquisition and preparation, present alternatives, and explain the reasons for the chosen approach. A successful implementation of such a tool can lead to a better understanding of customer cancellation behavior by the insurer, enabling more targeted actions in certain situations.

stat.AP↗

arXiv2026

Model-based bootstrap inference for Cox models after Lasso selection

Inference after variable selection in Cox regression is difficult because simple Wald-type intervals after selection can have poor finite-sample conditional coverage. We study a model-based bootstrap for inference after Cox-Lasso variable selection. The Cox-Lasso is fitted once to the original data to select a set of variables, after which an unpenalized Cox model is fitted using only those variables. Bootstrap samples are generated from a semiparametric plug-in Cox model specified by the coefficient estimate from this unpenalized Cox refit, the Breslow baseline cumulative hazard estimator, and a plug-in censoring distribution. In every bootstrap sample, the selected variable set is kept fixed and only the unpenalized Cox model is refitted. Under oracle-type sparse-model assumptions and standard Cox model regularity conditions, we prove first-order bootstrap validity for this procedure. In the simulation scenarios considered, percentile and studentized bootstrap intervals showed improved conditional coverage relative to the bootstrap-Wald interval in several small- and moderate-sample settings. Their performance was broadly competitive with debiased intervals, although the comparison depended on signal strength, tuning, and selection stability. A SEER breast cancer example illustrates that the procedure can be implemented in a realistic survival analysis and provides interpretable uncertainty quantification for effects reported after variable selection.

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arXiv2026

Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods

Statistical practice does not automatically follow methodological innovation. Regularization methods, widely advocated to reduce overfitting and stabilize inference, are readily available in modern software, but are not consistently used by data analysts. We investigate this implementation gap in a large-scale empirical study of trust in, and acceptance of, regularization techniques, based on $N = 606$ data analysts. Drawing on measurement frameworks from technology acceptance research, we survey practitioners and embed a randomized experiment to test whether written recommendation of regularization methods increases trust or intended use. We find no evidence of such an effect. Instead, adoption intentions are strongly associated with analysts' perceptions of ease of implementation and practical benefit, such as improved bias control or interpretability. Perceived social norms also emerge as a central driver. These results indicate that uptake of statistical methodology depends less on formal recommendations than on usability, perceived utility, and community practice.

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arXiv2025

Time-to-event prediction for grouped variables using Exclusive Lasso

The integration of high-dimensional genomic data and clinical data into time-to-event prediction models has gained significant attention due to the growing availability of these datasets. Traditionally, a Cox regression model is employed, concatenating various covariate types linearly. Given that much of the data may be redundant or irrelevant, feature selection through penalization is often desirable. A notable characteristic of these datasets is their organization into blocks of distinct data types, such as methylation and clinical predictors, which requires selecting a subset of covariates from each group due to high intra-group correlations. For this reason, we propose utilizing Exclusive Lasso regularization in place of standard Lasso penalization. We apply our methodology to a real-life cancer dataset, demonstrating enhanced survival prediction performance compared to the conventional Cox regression model.

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arXiv2025

Statistical enhanced learning for modeling and prediction tennis matches at Grand Slam tournaments

In this manuscript, we concentrate on a specific type of covariates, which we call statistically enhanced, for modeling tennis matches for men at Grand slam tournaments. Our goal is to assess whether these enhanced covariates have the potential to improve statistical learning approaches, in particular, with regard to the predictive performance. For this purpose, various proposed regression and machine learning model classes are compared with and without such features. To achieve this, we considered three slightly enhanced variables, namely elo rating along with two different player age variables. This concept has already been successfully applied in football, where additional team ability parameters, which were obtained from separate statistical models, were able to improve the predictive performance. In addition, different interpretable machine learning (IML) tools are employed to gain insights into the factors influencing the outcomes of tennis matches predicted by complex machine learning models, such as the random forest. Specifically, partial dependence plots (PDP) and individual conditional expectation (ICE) plots are employed to provide better interpretability for the most promising ML model from this work. Furthermore, we conduct a comparison of different regression and machine learning approaches in terms of various predictive performance measures such as classification rate, predictive Bernoulli likelihood, and Brier score. This comparison is carried out on external test data using cross-validation, rolling window, and expanding window strategies.

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arXiv2024

Boosting Distributional Copula Regression for Bivariate Right-Censored Time-to-Event Data

We propose a highly flexible distributional copula regression model for bivariate time-to-event data in the presence of right-censoring. The joint survival function of the response is constructed using parametric copulas, allowing for a separate specification of the dependence structure between the time-to-event outcome variables and their respective marginal survival distributions. The latter are specified using well-known parametric distributions such as the log-Normal, log-Logistic (proportional odds model), or Weibull (proportional hazards model) distributions. Hence, the marginal univariate event times can be specified as parametric (also known as Accelerated Failure Time, AFT) models. Embedding our model into the class of generalized additive models for location, scale and shape, possibly all distribution parameters of the joint survival function can depend on covariates. We develop a component-wise gradient-based boosting algorithm for estimation. This way, our approach is able to conduct data-driven variable selection. To the best of our knowledge, this is the first implementation of multivariate AFT models via distributional copula regression with automatic variable selection via statistical boosting. A special merit of our approach is that it works for high-dimensional (p>>n) settings. We illustrate the practical potential of our method on a high-dimensional application related to semi-competing risks responses in ovarian cancer. All of our methods are implemented in the open source statistical software R as add-on functions of the package gamboostLSS.

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arXiv2024

A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts

Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to ensure data reliability is the automatic detection of anomalies. While this approach is highly useful, it is also challenging due to the scarcity of labeled data that distinguish between normal and anomalous contracts or inter\-actions. This manuscript discusses several classical and modern unsupervised anomaly detection methods and compares their performance across two different datasets. In order to facilitate the adoption of these methods by companies, this work also explores ways to automate the process, making it accessible even to non-data scientists.

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arXiv2024

Modeling and Prediction of the UEFA EURO 2024 via Combined Statistical Learning Approaches

In this work, three fundamentally different machine learning models are combined to create a new, joint model for forecasting the UEFA EURO 2024. Therefore, a generalized linear model, a random forest model, and a extreme gradient boosting model are used to predict the number of goals a team scores in a match. The three models are trained on the match results of the UEFA EUROs 2004-2020, with additional covariates characterizing the teams for each tournament as well as three enhanced variables derived from different ranking methods for football teams. The first enhanced variable is based on historic match data from national teams, the second is based on the bookmakers' tournament winning odds of all participating teams, and the third is based on historic match data of individual players both for club and international matches, resulting in player ratings. Then, based on current covariate information of the participating teams, the final trained model is used to predict the UEFA EURO 2024. For this purpose, the tournament is simulated 100.000 times, based on the estimated expected number of goals for all possible matches, from which probabilities across the different tournament stages are derived. Our combined model identifies France as the clear favourite with a winning probability of 19.2%, followed by England (16.7%) and host Germany (13.7%).

cs.LG↗

arXiv2023

Statistically Enhanced Learning: a feature engineering framework to boost (any) learning algorithms

Feature engineering is of critical importance in the field of Data Science. While any data scientist knows the importance of rigorously preparing data to obtain good performing models, only scarce literature formalizes its benefits. In this work, we will present the method of Statistically Enhanced Learning (SEL), a formalization framework of existing feature engineering and extraction tasks in Machine Learning (ML). The difference compared to classical ML consists in the fact that certain predictors are not directly observed but obtained as statistical estimators. Our goal is to study SEL, aiming to establish a formalized framework and illustrate its improved performance by means of simulations as well as applications on real life use cases.

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arXiv2022

Using Sequential Statistical Tests for Efficient Hyperparameter Tuning

Hyperparameter tuning is one of the the most time-consuming parts in machine learning. Despite the existence of modern optimization algorithms that minimize the number of evaluations needed, evaluations of a single setting may still be expensive. Usually a resampling technique is used, where the machine learning method has to be fitted a fixed number of k times on different training datasets. The respective mean performance of the k fits is then used as performance estimator. Many hyperparameter settings could be discarded after less than k resampling iterations if they are clearly inferior to high-performing settings. However, resampling is often performed until the very end, wasting a lot of computational effort. To this end, we propose the Sequential Random Search (SQRS) which extends the regular random search algorithm by a sequential testing procedure aimed at detecting and eliminating inferior parameter configurations early. We compared our SQRS with regular random search using multiple publicly available regression and classification datasets. Our simulation study showed that the SQRS is able to find similarly well-performing parameter settings while requiring noticeably fewer evaluations. Our results underscore the potential for integrating sequential tests into hyperparameter tuning.

cs.LG↗

arXiv2022

Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important features

Life assurance companies typically possess a wealth of data covering multiple systems and databases. These data are often used for analyzing the past and for describing the present. Taking account of the past, the future is mostly forecasted by traditional statistical methods. So far, only a few attempts were undertaken to perform estimations by means of machine learning approaches. In this work, the individual contract cancellation behavior of customers within two partial stocks is modeled by the aid of various classification methods. Partial stocks of private pension and endowment policy are considered. We describe the data used for the modeling, their structured and in which way they are cleansed. The utilized models are calibrated on the basis of an extensive tuning process, then graphically evaluated regarding their goodness-of-fit and with the help of a variable relevance concept, we investigate which features notably affect the individual contract cancellation behavior.

stat.ML↗

arXiv2022

Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?

Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of multiple outputs the question arises whether we separately fit univariate models or directly follow a multivariate approach. For the latter, several possibilities exist that are, e.g. based on modified splitting or stopping rules for multi-output regression. In this work we compare these methods in extensive simulations to help in answering the primary question when to use multivariate ensemble techniques.

stat.ML↗

arXiv2021

Hybrid Machine Learning Forecasts for the UEFA EURO 2020

Three state-of-the-art statistical ranking methods for forecasting football matches are combined with several other predictors in a hybrid machine learning model. Namely an ability estimate for every team based on historic matches; an ability estimate for every team based on bookmaker consensus; average plus-minus player ratings based on their individual performances in their home clubs and national teams; and further team covariates (e.g., market value, team structure) and country-specific socio-economic factors (population, GDP). The proposed combined approach is used for learning the number of goals scored in the matches from the four previous UEFA EUROs 2004-2016 and then applied to current information to forecast the upcoming UEFA EURO 2020. Based on the resulting estimates, the tournament is simulated repeatedly and winning probabilities are obtained for all teams. A random forest model favors the current World Champion France with a winning probability of 14.8% before England (13.5%) and Spain (12.3%). Additionally, we provide survival probabilities for all teams and at all tournament stages.

cs.LG↗

arXiv2020

Deducing neighborhoods of classes from a fitted model

In todays world the request for very complex models for huge data sets is rising steadily. The problem with these models is that by raising the complexity of the models, it gets much harder to interpret them. The growing field of \emph{interpretable machine learning} tries to make up for the lack of interpretability in these complex (or even blackbox-)models by using specific techniques that can help to understand those models better. In this article a new kind of interpretable machine learning method is presented, which can help to understand the partitioning of the feature space into predicted classes in a classification model using quantile shifts. To illustrate in which situations this quantile shift method (QSM) could become beneficial, it is applied to a theoretical medical example and a real data example. Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed. By comparing the predictions before and after the manipulations, under certain conditions the observed changes in the predictions can be interpreted as neighborhoods of the classes with regard to the manipulated features. Chordgraphs are used to visualize the observed changes.

stat.ML↗

arXiv2020

Random boosting and random^2 forests -- A random tree depth injection approach

The induction of additional randomness in parallel and sequential ensemble methods has proven to be worthwhile in many aspects. In this manuscript, we propose and examine a novel random tree depth injection approach suitable for sequential and parallel tree-based approaches including Boosting and Random Forests. The resulting methods are called \emph{Random Boost} and \emph{Random$^2$ Forest}. Both approaches serve as valuable extensions to the existing literature on the gradient boosting framework and random forests. A Monte Carlo simulation, in which tree-shaped data sets with different numbers of final partitions are built, suggests that there are several scenarios where \emph{Random Boost} and \emph{Random$^2$ Forest} can improve the prediction performance of conventional hierarchical boosting and random forest approaches. The new algorithms appear to be especially successful in cases where there are merely a few high-order interactions in the generated data. In addition, our simulations suggest that our random tree depth injection approach can improve computation time by up to 40%, while at the same time the performance losses in terms of prediction accuracy turn out to be minor or even negligible in most cases.

stat.ML↗

arXiv2020

A flexible adaptive lasso Cox frailty model based on the full likelihood

In this work a method to regularize Cox frailty models is proposed that accommodates time-varying covariates and time-varying coefficients and is based on the full instead of the partial likelihood. A particular advantage in this framework is that the baseline hazard can be explicitly modeled in a smooth, semi-parametric way, e.g. via P-splines. Regularization for variable selection is performed via a lasso penalty and via group lasso for categorical variables while a second penalty regularizes wiggliness of smooth estimates of time-varying coefficients and the baseline hazard. Additionally, adaptive weights are included to stabilize the estimation. The method is implemented in R as coxlasso and will be compared to other packages for regularized Cox regression. Existing packages, however, do not allow for the combination of different effects that are accommodated in coxlasso.

stat.ME↗

arXiv2019

Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques

Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated in two general approaches, namely gradient boosting and likelihood-based boosting. An extensive framework has been proposed in order to fit generalised mixed models based on boosting, however for the case of cluster-constant covariates likelihood-based boosting approaches tend to mischoose variables in the selection step leading to wrong estimates. We propose an improved boosting algorithm for linear mixed models where the random effects are properly weighted, disentangled from the fixed effects updating scheme and corrected for correlations with cluster-constant covariates in order to improve quality of estimates and in addition reduce the computational effort. The method outperforms current state-of-the-art approaches from boosting and maximum likelihood inference which is shown via simulations and various data examples.

stat.ME↗

arXiv2019

A regularized hidden Markov model for analyzing the 'hot shoe' in football

Although academic research on the 'hot hand' effect (in particular, in sports, especially in basketball) has been going on for more than 30 years, it still remains a central question in different areas of research whether such an effect exists. In this contribution, we investigate the potential occurrence of a 'hot shoe' effect for the performance of penalty takers in football based on data from the German Bundesliga. For this purpose, we consider hidden Markov models (HMMs) to model the (latent) forms of players. To further account for individual heterogeneity of the penalty taker as well as the opponent's goalkeeper, player-specific abilities are incorporated in the model formulation together with a LASSO penalty. Our results suggest states which can be tied to different forms of players, thus providing evidence for the hot shoe effect, and shed some light on exceptionally well-performing goalkeepers, which are of potential interest to managers and sports fans.

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