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Time prediction models of grapple skidder HSM 904 using multiple linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS)

Naghdi, Ramin; Ghajar, Ismael; Tsioras, Petros A. - In: Operational research : an international journal 16 (2016) 3, pp. 501-512

Persistent link: https://www.econbiz.de/10011724645

Improved Approximation Methods for Tied Data in Continuous-Time Cox Regressions

Hurtado Rodriguez, Jose Eduardo - 2016

The Breslow (1974) and Efron (1977) approximations are two of the most widely-used methods for calculating the computationally-intensive partial likelihood function of continuous-time Cox (1972)-type proportional hazards regressions in the presence of tied data. This article develops analytical...

Persistent link: https://www.econbiz.de/10012992464

Are box office revenues equally unpredictable for all movies? : evidence from a random forest-based model

Antipov, Evengy A.; Pokryshevskaya, Elena B. - In: Journal of revenue and pricing management 16 (2017) 3, pp. 295-307

Persistent link: https://www.econbiz.de/10011912835

Predicting and explaining pig iron production on charcoal blast furnaces : a machine learning approach

Lima, Marcio Salles Melo; Eryarsoy, Enes; Delen, Dursun - In: INFORMS journal on applied analytics 51 (2021) 3, pp. 213-235

Persistent link: https://www.econbiz.de/10012596350

Carsharing customer demand forecasting using causal, time series and neural network methods : a case study

Moein, Elnaz; Awasthi, Anjali - In: International journal of services and operations … 35 (2020) 1, pp. 36-57

Persistent link: https://www.econbiz.de/10012178607

Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link

Hainaut, Donatien; Trufin, Julien; Denuit, Michel - In: Scandinavian actuarial journal 2022 (2022) 10, pp. 841-866

Persistent link: https://www.econbiz.de/10013491040

Sustainable investing and the cross-section of returns and maximum drawdown

Goldberg, Lisa; Mouti, Saad - In: The Journal of finance and data science : JFDS 8 (2022), pp. 353-387

We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and...

Persistent link: https://www.econbiz.de/10014433739

Unobserved heterogeneity in regression models : a semiparametric approach based on nonlinear sieves

Assunção, Juliano J.; Burity, Priscila; Medeiros, … - In: Brazilian review of econometrics : BRE ; the review of … 35 (2015) 1, pp. 47-63

Persistent link: https://www.econbiz.de/10011538815

Combination forecasts of tourism demand with machine learning models

Claveria, Oscar; Monte, Enric; Torra, Salvador - In: Applied economics letters 23 (2016) 4/6, pp. 428-431

Persistent link: https://www.econbiz.de/10011430763

Nonlinear time series and neural-network models of echange rates between the US Dollar and major currencies

Allen, David E.; McAleer, Michael; Peiris, Shelton; … - In: Risks : open access journal 4 (2016) 1, pp. 1-14

, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models … relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate …

Persistent link: https://www.econbiz.de/10011443686