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  • Search: subject:"Variable importance"
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Year of publication
Subject
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variable importance 20 Forecasting model 19 Prognoseverfahren 19 Variable importance 19 Artificial intelligence 13 Künstliche Intelligenz 13 Theorie 13 Theory 13 machine learning 10 Neural networks 6 Neuronale Netze 6 Forestry 5 Forstwirtschaft 5 Random forest 5 Variable Importance 5 random forest 5 Regression analysis 4 Regressionsanalyse 4 Risiko 4 Risk 4 Algorithm 3 Algorithmus 3 Attendance 3 Capital income 3 Data Mining 3 Data mining 3 Estimation 3 Forest policy 3 Forstpolitik 3 Kapitaleinkommen 3 Learning process 3 Lernprozess 3 Machine learning 3 Neural network 3 Schätzung 3 loss function 3 Airbnb listing 2 Aktienmarkt 2 Arbeitslosenversicherung 2 Arbeitsuche 2
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Online availability
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Undetermined 30 Free 22 CC license 4
Type of publication
All
Article 39 Book / Working Paper 13
Type of publication (narrower categories)
All
Article in journal 25 Aufsatz in Zeitschrift 25 Working Paper 12 Arbeitspapier 8 Graue Literatur 8 Non-commercial literature 8 Article 2 research-article 1
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Language
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English 41 Undetermined 11
Author
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Borup, Daniel 4 Montes Schütte, Erik Christian 4 Rapach, David E. 4 Chattopadhyay, Manojit 3 Chlebus, Marcin 3 Laan, Mark van der 3 Mitra, Subrata Kumar 3 Mueller, Steffen Q. 3 Carroll, J. 2 DeSarbo, Wayne 2 Goulet Coulombe, Philippe 2 Ickstadt, Katja 2 Knapp, Sabine 2 Rojahn, Joachim 2 Schwender, Holger 2 Schwenk-Nebbe, Sander 2 Velden, Michel van de 2 Xie, Shengkun 2 Al Janabi, Mazin A. M. 1 Alexy, Martin 1 Bakalli, Gaetan 1 Benkeser, David 1 Birkner, Merrill 1 Borgonovo, Emanuele 1 Borowski, Piotr 1 Boulesteix, Anne-Laure 1 Bui, Dien Giau 1 Buuren, Stef 1 Ceglia, Cesarina 1 Chae, Bongsug 1 Chi, Guotai 1 Clark, Linda 1 Dinçer, Hasan 1 Emir, Senol 1 Erdem, Diba 1 Estran, Rémy 1 Fabian, Piotr 1 Fan, Juanjuan 1 Feng, Lingbing 1 Figalli, Alessio 1
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Institution
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Institut für Wirtschafts- und Sozialstatistik, Universität Dortmund 1
Published in...
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Working papers 4 International Journal of Biostatistics 3 Computational Statistics & Data Analysis 2 International journal of hospitality management 2 Journal of Classification 2 Psychometrika 2 Applied economics 1 Asia-Pacific journal of financial studies 1 CREATES research paper 1 Corporate ownership & control : international scientific journal 1 Discussion Paper 1 Econometric Institute research papers 1 Econometrics : open access journal 1 Finance research letters 1 Financial analysts journal : FAJ 1 Hamburg Contemporary Economic Discussions 1 Hamburg contemporary economic discussions 1 International journal of finance & banking studies : JJFBS 1 International journal of finance & economics : IJFE 1 International journal of forecasting 1 International review of economics & finance : IREF 1 International review of financial analysis 1 Journal of Causal Inference 1 Journal of business & economic statistics : JBES ; a publication of the American Statistical Association 1 Journal of forecasting 1 Journal of hospitality & tourism research : JHTR ; the professional journal of the Council on Hotel, Restaurant, and Institutional Education 1 Journal of industry, competition and trade 1 Journal of supply chain management science : JSCMS 1 Management science : journal of the Institute for Operations Research and the Management Sciences 1 Managerial finance 1 Maritime policy & management 1 Pacific-Basin finance journal 1 Research paper series / Swiss Finance Institute 1 Risks 1 Risks : open access journal 1 Social Indicators Research 1 Statistical Methods and Applications 1 Statistics in Transition New Series 1 Studies in economics and finance 1 Swiss Finance Institute Research Paper 1
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Source
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ECONIS (ZBW) 33 RePEc 12 EconStor 6 Other ZBW resources 1
Showing 1 - 10 of 52
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Bayesian Panel Variable Selection under model uncertainty for high-dimensional data
Pathairat Pastpipatkul; Htwe Ko - In: Econometrics : open access journal 14 (2026) 1, pp. 1-17
inference, which jointly accounts for variable importance ranking. Both the simulation studies and the empirical application to …
Persistent link: https://www.econbiz.de/10015640470
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Shapley curves : a smoothing perspective
Miftachov, Ratmir; Keilbar, Georg; Härdle, Wolfgang - In: Journal of business & economic statistics : JBES ; a … 43 (2025) 2, pp. 312-323
Persistent link: https://www.econbiz.de/10015534140
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Forecasting the direction of the Fed's monetary policy decisions using random forest
Yoon, Jungyeon; Fan, Juanjuan - In: Journal of forecasting 43 (2024) 7, pp. 2848-2859
Persistent link: https://www.econbiz.de/10015110569
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Exploration of machine learning methods for maritime risk predictions
Knapp, Sabine; Velden, Michel van de - In: Maritime policy & management 51 (2024) 7, pp. 1443-1473
Persistent link: https://www.econbiz.de/10015441977
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Rethinking variable importance in machine learning : an economic perspective on empirical asset pricing
Jo, Yonghwan; Kim, Yong Hwi - In: Financial analysts journal : FAJ 82 (2026) 2, pp. 92-135
Persistent link: https://www.econbiz.de/10015647384
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Global sensitivity analysis via optimal transport
Borgonovo, Emanuele; Figalli, Alessio; Plischke, Elmar; … - In: Management science : journal of the Institute for … 71 (2025) 5, pp. 3809-3828
Persistent link: https://www.econbiz.de/10015446271
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SMEs' failure prediction : are machine learning models superior?
Káčer, Marek; Alexy, Martin - In: Studies in economics and finance 42 (2025) 5, pp. 908-944
Persistent link: https://www.econbiz.de/10015576159
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The anatomy of out-of-sample forecasting accuracy
Borup, Daniel; Goulet Coulombe, Philippe; Rapach, David E. - 2022
We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...
Persistent link: https://www.econbiz.de/10014278179
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Balancing and variable reduction of firm bankruptcy data
Olson, David L.; Chae, Bongsug - In: Journal of supply chain management science : JSCMS 3 (2022) 1/2, pp. 3-15
Financial stress experienced by supply chain elements causes stress to all members. Predictive data mining is a common tool for predicting bankruptcy. Bankruptcy often involves highly imbalanced datasets with a large number of potential variables, with bankrupt firms being by far the minority...
Persistent link: https://www.econbiz.de/10013447669
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Corporate loan duration, macroeconomic environments, and COVID-19
Kim, Dongwoo - In: International review of economics & finance : IREF 93 (2024) 2, pp. 1088-1103
Persistent link: https://www.econbiz.de/10014535687
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