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50th Scientific Meeting of the Italian Statistical Society, SIS 2021

作者： Templ, Matthias Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8404 Switzerland

ISBN: (纸本)9783031166082

The application of non-compositional methods to compositional data representing parts of a whole should be avoided from a theoretical point of view. For example, one can show that Pearson correlations applied to compositional data are biased to be negative. Moreover, almost all statistical methods lead to biased estimates when applied to compositional data. One way out is to analyze data after representing them in log-ratio coordinates. However, several implications arise, such as interpretation in log-ratios and dealing with zeros and non-detects where log-ratios are undefined. When focusing on settings where only the prediction and classification error is important rather than an interpretation of results, one might argue to rather use non-linear methods to avoid those implications. Generally, it is known that misclassification and prediction errors are lower with a log-ratio approach when using machine learning methods that model the linear relationship between variables. However, is this also true when training a neural network who may learn the inner relationships between parts of a whole also without representing the data in log-ratios? This paper gives an answer of this matter based on applications with multiple real data sets, leading to the recommendation to use a compositional treatment of compositional data in any case. Misclassification and prediction errors are lower when nonlinear methods, and in particular deep learning methods, are applied together with a compositional treatment of compositional data. The compositional treatment of compositional data therefore remains very important even in the context of focusing on prediction errors using deep artificial neural networks or other nonlinear methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural networks

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What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice 24

What's Distributive Justice Got to Do with It? Rethinking Al...

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Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society

作者： Corinna Hertweck Christoph Heitz Michele Loi Institute for Data Analysis and Process Design Zurich University of Applied Sciences Winterthur Switzerland and Department of Informatics University of Zurich Zurich Switzerland Institute for Data Analysis and Process Design Zurich University of Applied Sciences Winterthur Switzerland AlgorithmWatch Berlin Germany

In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes, their proposal is only accompanied by a loose link to ideas from moral philosophy - which makes it difficult to understand when the proposed criteria should be used to evaluate the fairness of a decision-making system. More recently, researchers have thus retroactively tried to tie existing fairness criteria to philosophical concepts. Group fairness criteria have typically been linked to egalitarianism, a theory of distributive justice. This makes it tempting to believe that fairness criteria mathematically represent ideals of distributive justice and this is indeed how they are typically portrayed. In this paper, we will discuss why the current approach of linking algorithmic fairness and distributive justice is too simplistic and, hence, insufficient. We argue that in the context of imperfect decision-making systems - which is what we deal with in algorithmic fairness - we should not only care about what the ideal distribution of benefits/harms among individuals would look like but also about how deviations from said ideal are distributed. Our claim is that algorithmic fairness is concerned with unfairness in these deviations. This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.

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Regime-dependent drivers of the EUR/CHF exchange rate

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Swiss Journal of Economics and Statistics 2023年第1期159卷 3页

作者： Kotlarz, Piotr Hanke, Michael Stöckl, Sebastian Liechtenstein Business School University of Liechtenstein Fürst-Franz-Josef-Strasse Vaduz Liechtenstein Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 71 Winterthur Switzerland

We analyze drivers of the EUR/CHF exchange rate in different regimes between 2000 and 2020. Structural breaks between these subperiods are estimated in an integrated way together with the drivers that are relevant during these subperiods. Overall, the main drivers of the exchange rate include European equity and volatility indices, interest rate and term structure slope differentials, as well as monetary policy interventions. For the “peg period” September 2011–January 2015, in addition to the observed exchange rate we also analyze the drivers of the latent exchange rate that could have been observed in the absence of the peg. Interestingly, the SNB’s foreign currency investments became a significant driver of the EUR/CHF exchange rate only after the end of the peg period when there was no longer an officially communicated target rate. © 2023, The Author(s).

关键词： EUR/CHF exchange rate Exchange rate drivers Foreign currency interventions Latent exchange rate Monetary policy

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Assessing Keyness using Permutation Tests

arXiv

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arXiv 2023年

作者： Mildenberger, Thoralf Institute of Data Analysis and Process Design Switzerland ZHAW Zurich University of Applied Sciences Switzerland

We propose a resampling-based approach for assessing keyness in corpus linguistics based on suggestions by Gries (2006, 2022). Traditional approaches based on hypothesis tests (e.g. Likelihood Ratio) model the copora as independent identically distributed samples of tokens. This model does not account for the often observed uneven distribution of occurences of a word across a corpus. When occurences of a word are concentrated in few documents, large values of LLR and similar scores are in fact much more likely than accounted for by the token-by-token sampling model, leading to false positives. We replace the token-by-token sampling model by a model where corpora are samples of documents rather than tokens, which is much closer to the way corpora are actually assembled. We then use a permutation approach to approximate the distribution of a given keyness score under the null hypothesis of equal frequencies and obtain p-values for assessing significance. We do not need any assumption on how the tokens are organized within or across documents, and the approach works with basically any keyness score. Hence, appart from obtaining more accurate p-values for scores like LLR, we can also assess significance for e.g. the logratio which has been proposed as a measure of effect size. An efficient implementation of the proposed approach is provided in the R package keyperm available from github. Copyright © 2023, The Authors. All rights reserved.

关键词： Lunar surface analysis

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High-Frequency Causality in the VIX Index and its derivatives: Empirical Evidence

arXiv

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arXiv 2022年

作者： Farokhnia, Kia Osterrieder, Joerg Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Switzerland

In February 2018, the VIX index has seen its largest ever increase and has lead to significant losses for some major volatility-related products. Despite many efforts, the precise underlying reasons are yet to be discovered. We study the role of linear causality in the VIX index and its derivatives during January and February 2018. Due to the shortcomings of statistical inferences for stochastic volatility models, the dynamics of the volatility expectation index VIX remain controversial. Leveraging intraday data, we discover novel empirical results describing their interaction. We find bidirectional causality between the VIX spot and the implied volatility of Standard & Poor's 500 options, suggesting a volatility feedback effect. The spot index tends to be lagging its own futures, while the vector autoregression's error correction mechanism reveals a significant mean-reverting equilibrium relationship. The evidence is consistent with recent theories indicating that implied volatility has stronger feedback than realized volatility. The paper reveals a retroactive information flow and highlights novel insights for the market microstructure of VIX derivatives and their related S&P 500 options. © 2022, CC0.

关键词： Error correction

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Greening urban logistics: Introducing a method to evaluate optimised decoupling hub locations

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Transportation Research Procedia 2023年 72卷 572-579页

作者： Maike Scherrer Albert Steiner Viola Rühlin Zurich University of Applied Sciences School of Engineering Institute of Sustainable Development Technikumstrasse 9 8401 Winterthur Switzerland Zurich University of Applied Sciences School of Engineering Institute of Data Analysis and Process Design Rosenstrasse 3 8401 Winterthur Switzerland

Sustainable urban logistics has become challenging over the course of the last decades due to logistics sprawl and increased demands of goods in urban areas. To overcome this problem, a fine-grained network of decoupling hubs, separating suppliers and customers in a spacial and timely manner, are needed. The paper at hand introduces a model of how to calculate optimal decoupling hub locations within urban areas with the goal that inhabitants walk or cycle to the next decoupling hub and do not take a motorised vehicle. The study at hand uses the northern district of Zurich as unit of analysis and shows that by using the developed model, 80% of the inhabitants need to walk less than 250 metres to their next decoupling hub, which is within the radius of 100 metres to the next public transport stop. This supports the integration of picking up or dropping off deliveries while commuting.

关键词： decoupling hubs sustainable urban logistics facility location probem

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Resource Allocation with Karma Mechanisms

arXiv

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arXiv 2024年

作者： Riehl, Kevin Kouvelas, Anastasios Makridis, Michail Traffic Engineering Group Institute for Transport Planning and Systems ETH Zurich Stefano-Franscini-Platz 5 Zurich8093 Switzerland Transport and Traffic Engineering Group Institute of Data Analysis and Process Design Zurich University of Applied Sciences Technikumstrasse 81 Winterthur8400 Switzerland

Monetary markets serve as established resource allocation mechanisms, typically achieving efficient solutions with limited information. However, they are susceptible to market failures, particularly under the presence of public goods, externalities, or inequality of economic power. Moreover, in many resource allocating contexts, money faces social, ethical, and legal constraints. Consequently, research increasingly explores artificial currencies and non-monetary markets, with Karma emerging as a notable concept. Karma, a non-tradeable, resource-inherent currency for prosumer resources, operates on the principles of contribution and consumption of specific resources. It embodies fairness, near incentive compatibility, Pareto-efficiency, robustness to population heterogeneity, and can incentivize a reduction in resource scarcity. The literature on Karma is scattered across disciplines, varies in scope, and lacks of conceptual clarity and coherence. Thus, this study undertakes a comprehensive review of the Karma mechanism, systematically comparing its resource allocation applications and elucidating overlooked mechanism design elements. Through a systematic mapping study, this review situates Karma within its literature context, offers a structured design parameter framework, and develops a road-map for future research directions. © 2024, CC BY-NC-ND.

关键词： Resource allocation

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Deep Interpretable Ensembles

arXiv

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arXiv 2022年

作者： Kook, Lucas Götschi, Andrea Baumann, Philipp F. M. Hothorn, Torsten Sick, Beate Epidemiology Biostatistics & Prevention Institute University of Zurich CH-8001 Switzerland Institute for Data Analysis and Process Design Zurich University of Applied Sciences CH-8400 Switzerland KOF Swiss Economic Institute ETH Zurich CH-8092 Switzerland

Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually black box neural networks, or recently, partially interpretable semi-structured deep transformation models. However, interpretability of the ensemble members is generally lost upon aggregation. This is a crucial drawback of deep ensembles in high-stake decision fields, in which interpretable models are desired. We propose a novel transformation ensemble which aggregates probabilistic predictions with the guarantee to preserve interpretability and yield uniformly better predictions than the ensemble members on average. Transformation ensembles are tailored towards interpretable deep transformation models but are applicable to a wider range of probabilistic neural networks. In experiments on several publicly available data sets, we demonstrate that transformation ensembles perform on par with classical deep ensembles in terms of prediction performance, discrimination, and calibration. In addition, we demonstrate how transformation ensembles quantify both aleatoric and epistemic uncertainty, and produce minimax optimal predictions under certain conditions. © 2022, CC BY.

关键词： Uncertainty analysis

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Deep transformation models for functional outcome prediction after acute ischemic stroke

arXiv

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arXiv 2022年

作者： Herzog, Lisa Kook, Lucas Götschi, Andrea Petermann, Katrin Hänsel, Martin Hamann, Janne Dürr, Oliver Wegener, Susanne Sick, Beate Epidemiology Biostatistics & Prevention Institute University of Zurich Switzerland Institute for Data Analysis and Process Design Zurich University of Applied Sciences Switzerland Department of Neurology University Hospital Zurich Switzerland Insitute for Optical Systems Konstanz University of Applied Sciences Germany

In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and image data. We show how to apply deep transformation models (DTMs) for distributional regression which fulfill these requirements. DTMs allow the data analyst to specify (deep) neural networks for different input modalities making them applicable to various research questions. Like statistical models, DTMs can provide interpretable effect estimates while achieving the state-of-the-art prediction performance of deep neural networks. In addition, the construction of ensembles of DTMs that retain model structure and interpretability allows quantifying epistemic and aleatoric uncertainty. In this study, we compare several DTMs, including baseline-adjusted models, trained on a semi-structured data set of 407 stroke patients with the aim to predict ordinal functional outcome three months after stroke. We follow statistical principles of model-building to achieve an adequate trade-off between interpretability and flexibility while assessing the relative importance of the involved data modalities. We evaluate the models for an ordinal and dichotomized version of the outcome as used in clinical practice. We show that both, tabular clinical and brain imaging data, are useful for functional outcome prediction, while models based on tabular data only outperform those based on imaging data only. There is no substantial evidence for improved prediction when combining both data modalities. Overall, we highlight that DTMs provide a powerful, interpretable approach to analyzing semi-structured data and that they have the potential to support clinical decision making. © 2022, CC BY.

关键词： Deep neural networks

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Road network disruptions during extreme flooding events and their impact on the access to emergency medical services: A spatiotemporal vulnerability analysis

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Science of the Total Environment 2024年 956卷 177140页

作者： Papilloud, Tsolmongerel Steiner, Albert Zischg, Andreas Keiler, Margreth University of Bern Institute of Geography Hallerstrasse 12 BernCH-3012 Switzerland University of Bern Oeschger Centre for Climate Change Research Mobiliar Lab for Natural Risks Hochschulstrasse 4 BernCH-3012 Switzerland Zurich University of Applied Sciences ZHAW Institute of Data Analysis and Process Design Technikumstrasse 81 WinterthurCH-8401 Switzerland University of Innsbruck Department of Geography Innrain 52f Innsbruck6020 Austria Austrian Academy of Sciences Institute of Interdisciplinary Mountain Research Innrain 25 Innsbruck6020 Austria

The impact of extreme floods can be significant, affecting humans, the environment and any type of human-built infrastructure. A further consequence of flooding is restricted access to medical facilities, including constraints to access more dedicated emergency medical services (EMS). To date, there has been a lack of investigation into EMS accessibility dynamics during extreme floods. The objective of this study is to gain a deeper understanding of the spatiotemporal changes in accessibility-based vulnerability related to EMS during a representative extreme flood event, which was simulated over a period of six days. The study assesses the spatial accessibility of EMS centres to populations located within 15-, 30-, 45-, and 60-minute travel time thresholds in Canton Bern, Switzerland, and applies floating catchment area method. The dynamic aspects of EMS access vulnerability during extreme floods were assessed in two different ways. Firstly, the ratios between accessibility change and accessibility under normal conditions in 1 km grid cells were calculated at hourly moments, t. Subsequently, the resulting values were used to calculate the average vulnerability score. Secondly, percentage changes of affected populations were evaluated for different accessibility classes during the flood event and under all-time high static flood conditions. Varied spatial patterns of accessibility were generally observed with respect to the road network and population distributions in the hilly and mountainous landscape. Extending evaluations to consider temporal dynamics revealed a complex pattern of accessibility gains and losses in different regions of the study area, including those where no direct flood impacts occurred on the road network. The application of a static, all-time high flood condition resulted in an overestimation of accessibility limits to EMS centres. While this overestimation was not considered to be critical, the application of a spatiotemporal accessibility-ba

关键词： Travel time

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