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Mollins, Jeffrey; Lumb, Rachit - 2024

residual seasonality in each series. While no method works perfectly for every series, some methods are generally effective at … removing seasonality in weekly data, despite the increased difficulty of accounting for the shock of the COVID-19 pandemic …

Persistent link: https://www.econbiz.de/10015115015

Fehlende Werte in der angewandten Statistik

Schwab, Georg - 1991

Persistent link: https://www.econbiz.de/10000821227

Multiple imputation of missing data for multilevel models : simulations and recommendations

Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander - In: Organizational research methods : ORM 21 (2018) 1, pp. 111-149

Persistent link: https://www.econbiz.de/10011880091

Modeling and forecasting of Brazilian reservoir inflows via dynamic linear models

Lima, L. M. Marangon; Popova, Elmira; Damien, Paul - In: International journal of forecasting 30 (2014) 3, pp. 464-476

Persistent link: https://www.econbiz.de/10010511556

More data or better data? : a statistical decision problem

Dominitz, Jeff; Manski, Charles F. - In: The review of economic studies 84 (2017) 4, pp. 1583-1605

Persistent link: https://www.econbiz.de/10011920086

Multiple Imputation as a Solution to the Missing Data Problem in the Social Sciences

Tufis, Claudiu - 2014

In this paper I analyse a series of techniques designed for replacing missing data. From the extensive literature on political values in post- communist countries, I selected one of the most discussed models – the one proposed by Reisinger et al. (1994). In analysing political values in Russia...

Persistent link: https://www.econbiz.de/10014142591