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Subject
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parallel model 2 ARMA model 1 ARMA-Modell 1 Asymptotic properties 1 Bayesian methods 1 Demand 1 Forecasting model 1 Nachfrage 1 Neural networks 1 Neuronale Netze 1 Non-compliance behavior 1 Non-randomized response technique 1 Prognoseverfahren 1 Rasch test model 1 SANN 1 SARIMA 1 Saisonale Schwankungen 1 Seasonal variations 1 The parallel model 1 Theorie 1 Theory 1 Time series analysis 1 Unmatched count technique 1 Zeitreihenanalyse 1 demand forecasting 1 reaction time distributions 1 seasonal artificial neural network 1 seasonal autoregressive integrated moving average 1 serial model 1 series model 1
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Undetermined 3
Type of publication
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Article 3
Type of publication (narrower categories)
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Article in journal 1 Aufsatz in Zeitschrift 1
Language
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Undetermined 2 English 1
Author
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Amindoust, Atefeh 1 Bahrami, Maryam 1 Khashei, Mehdi 1 Liu, Yin 1 Schwarz, Wolfgang 1 Tian, Guo-Liang 1 Vorberg, Dirk 1
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Published in...
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Computational Statistics & Data Analysis 1 International journal of process management and benchmarking : IJPMB 1 Psychometrika 1
Source
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RePEc 2 ECONIS (ZBW) 1
Showing 1 - 3 of 3
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Demand forecasting using modelling and comparison of series and parallel structures of SARIMA and SMLP
Bahrami, Maryam; Khashei, Mehdi; Amindoust, Atefeh - In: International journal of process management and … 13 (2023) 2, pp. 282-301
Persistent link: https://www.econbiz.de/10014313808
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A variant of the parallel model for sample surveys with sensitive characteristics
Liu, Yin; Tian, Guo-Liang - In: Computational Statistics & Data Analysis 67 (2013) C, pp. 115-135
A new non-randomized response (NRR) model (called a variant of the parallel model) is proposed. The survey design and …. Theoretical and numerical comparisons showed that the proposed variant of the parallel model over-performs two existing NRR …
Persistent link: https://www.econbiz.de/10010871419
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Rasch-representable reaction time distributions
Vorberg, Dirk; Schwarz, Wolfgang - In: Psychometrika 55 (1990) 4, pp. 617-632
Persistent link: https://www.econbiz.de/10005757843
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