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  1. Swag: a wrapper method for sparse learning
    Autor*in: Molinari, Roberto; Bakalli, Gaetan; Guerrier, Stéphane; Miglioli, Cesare; Orso, Samuel; Scaillet, Olivier
    Erschienen: 2020
    Verlag:  Swiss Finance Institute, Geneva

    Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    VS 544
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    Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict the response of interest, an important branch of research is focused on sparse learning in order to avoid overfitting which can greatly affect the accuracy of out-of-sample prediction. However, in many practical settings we believe that only an extremely small combination of different attributes affect the response whereas even sparse-learning methods can still preserve a high number of attributes in high-dimensional settings and possibly deliver inconsistent prediction performance. As a consequence, the latter methods can also be hard to interpret for researchers and practitioners, a problem which is even more relevant for the “black-box”-type mechanisms of many learning approaches. Finally, aside from needing to quantify prediction uncertainty, there is often a problem of replicability since not all data-collection procedures measure (or observe) the same attributes and therefore cannot make use of proposed learners for testing purposes. To address all the previous issues, we propose to study a procedure that combines screening and wrapper methods and aims to find a library of extremely low-dimensional attribute combinations (with consequent low data collection and storage costs) in order to (i) match or improve the predictive performance of any particular learning method which uses all attributes as an input (including sparse learners); (ii) provide a low-dimensional network of attributes easily interpretable by researchers and practitioners; and (iii) increase the potential replicability of results due to a diversity of attribute combinations defining strong learners with equivalent predictive power. We call this algorithm “Sparse Wrapper AlGorithm” (SWAG)

     
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    Sprache: Englisch
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    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 20, 49
    Swiss Finance Institute Research Paper ; No. 20-49
    Umfang: 1 Online-Ressource (circa 19 Seiten), Illustrationen
  2. Multi-signal approaches for repeated sampling schemes in inertial sensor calibration
    Autor*in: Bakalli, Gaetan; Cuccio, Davide; Radi, Ahmed; El-Sheimy, Naser; Molinari, Roberto; Scaillet, Olivier; Guerrier, Stéphane
    Erschienen: 2021
    Verlag:  Swiss Finance Institute, Geneva

    Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 544
    Fernleihe:
    keine Fernleihe

     

    Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. Assuming the model structure has been identified, in this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution

     
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    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 21, 70
    Schlagworte: Generalized Method of Wavelet Moments; Inertial Sensor Calibration; Stochastic Error; Extended Kalman Filter; Navigation
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (circa 16 Seiten), Illustrationen
  3. Non-standard errors
    Autor*in: Menkveld, Albert J.; Dreber, Anna; Holzmeister, Felix; Huber, Jürgen; Johannesson, Magnus; Kirchler, Michael; Neusüß, Sebastian; Razen, Michael; Weitzel, Utz; Abad Díaz, David; Abudy, Menachem Meni; Adrian, Tobias; Aït-Sahalia, Yacine; Akmansoy, Olivier; Alcock, Jamie T.; Alexeev, Vitali; Aloosh, Arash; Amato, Livia; Amaya, Diego; Angel, James Joseph; Avetikian, Alejandro T.; Bach, Amadeus; Baidoo, Edwin; Bakalli, Gaetan; Bao, Li; Barbon, Andrea; Bashchenko, Oksana; Bindra, Parampreet Christopher; Bjønnes, Geir H.
    Erschienen: 2021
    Verlag:  Swiss Finance Institute, Geneva

    In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Fernleihe:
    keine Fernleihe

     

    In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

     
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    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 22, 09
    Schlagworte: Statistischer Fehler; Streuungsmaß; Wissenschaftler; Wissenschaftliche Methode; Theorie; non-standard errors; multi-analyst approach; liquidity
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (circa 58 Seiten), Illustrationen
  4. Non-standard errors
    Autor*in: Menkveld, Albert J.; Dreber, Anna; Holzmeister, Felix; Huber, Jürgen; Johannesson, Magnus; Kirchler, Michael; Neusüß, Sebastian; Razen, Michael; Weitzel, Utz; Abad Díaz, David; Abudy, Menachem Meni; Adrian, Tobias; Aït-Sahalia, Yacine; Akmansoy, Olivier; Alcock, Jamie T.; Alexeev, Vitali; Aloosh, Arash; Amato, Livia; Amaya, Diego; Angel, James Joseph; Avetikian, Alejandro T.; Bach, Amadeus; Baidoo, Edwin; Bakalli, Gaetan; Bao, Li; Barbon, Andrea; Bashchenko, Oksana; Bindra, Parampreet Christopher; Bjønnes, Geir H.
    Erschienen: November, 2021
    Verlag:  School of Finance, University of St. Gallen, St. Gallen

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Schriftenreihe: Working papers on finance / Swiss Institute of Banking and Finance (S/BF - HSG) ; no. 2021, 17
    Schlagworte: Statistischer Fehler; Streuungsmaß; Wissenschaftler; Wissenschaftliche Methode; Theorie; non-standard errors; multi-analyst approach; liquidity
    Umfang: 1 Online-Ressource (circa 57 Seiten), Illustrationen
  5. A penalized two-pass regression to predict stock returns with time-varying risk premia
    Autor*in: Bakalli, Gaetan; Guerrier, Stéphane; Scaillet, Olivier
    Erschienen: 2021
    Verlag:  Swiss Finance Institute, Geneva

    We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 544
    Fernleihe:
    keine Fernleihe

     

    We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
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    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 21, 09
    Schlagworte: two-pass regression; predictive modeling; large panel; factor model; LASSO penalization
    Umfang: 1 Online-Ressource (circa 38 Seiten), Illustrationen