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  • Search: subject:"biased sampling"
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Year of publication
Subject
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Lotsizing and scheduling 2 Produktionssteuerung 2 Simulation 2 Theorie 2 backward algorithm / local search 2 biased sampling 2 biased sampling via randomized regrets / Monte Carlo Simulation 2 fire occurrence probabilities 2 fire weather 2 forest fires 2 hypothesis testing / sequential analysis 2 Asymptotic Theory 1 Auxiliary information 1 Bias 1 Biased Sampling 1 Biased sampling 1 Brand 1 Case-control Data 1 Density-ratio model 1 Distortion Function 1 Doubly robust estimator 1 Estimation 1 Estimation theory 1 Fragmentation 1 Kalifornien 1 Logistic Regression 1 Missing data 1 Naturkatastrophe 1 Production control 1 Profile Log Likelihood 1 Prognoseverfahren 1 Propensity score 1 Robust statistics 1 Robustes Verfahren 1 Sampling 1 Schätztheorie 1 Schätzung 1 Semiparametric 1 Statistics 1 Stichprobenerhebung 1
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Online availability
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Free 7
Type of publication
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Book / Working Paper 7
Type of publication (narrower categories)
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Working Paper 4 Arbeitspapier 2 Graue Literatur 2 Non-commercial literature 2 Thesis 1
Language
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English 6 German 1
Author
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Drexl, Andreas 2 Haase, Knut 2 Ho, Joanne 2 Odening, Martin 2 Caulier, Jean-François 1 Hoshino, Takahiro 1 Kedem, Benjamin 1 Lu, Guanhua 1 Shimizu, Yuya 1
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Institution
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Centre d'Économie de la Sorbonne, Université Paris 1 (Panthéon-Sorbonne) 1 Sonderforschungsbereich 649: Ökonomisches Risiko, Wirtschaftswissenschaftliche Fakultät 1
Published in...
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Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 2 Documents de travail du Centre d'Economie de la Sorbonne 1 KEIO-IES discussion paper series 1 SFB 649 Discussion Paper 1 SFB 649 Discussion Papers 1
Source
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ECONIS (ZBW) 2 EconStor 2 RePEc 2 BASE 1
Showing 1 - 7 of 7
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Doubly robust-type estimation of population moments and parameters in biased sampling
Hoshino, Takahiro; Shimizu, Yuya - 2019
Persistent link: https://www.econbiz.de/10012171733
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The Interpretation of the Laakso-Taagepera Effective Number of Parties.
Caulier, Jean-François - Centre d'Économie de la Sorbonne, Université Paris 1 … - 2011
In this paper, we present a general statistical framework within which we can draw a new interpretation of the Laakso-Taagepera effective number of parties fragmentation index. With the particular method of sampling with probability proportional to the party sizes, we show that the...
Persistent link: https://www.econbiz.de/10008835201
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Weather-based estimation of wildfire risk
Ho, Joanne; Odening, Martin - 2009
Catastrophic wildfires in California have become more frequent in past decades, while insured losses per event have been rising substantially. On average, California ranks the highest among states in the U.S. in the number of fires as well as the number of acres burned each year. The study of...
Persistent link: https://www.econbiz.de/10010270697
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Weather-based estimation of wildfire risk
Ho, Joanne; Odening, Martin - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2009
forecast wildfire occurrences based on weather information that is readily available at low cost. Keywords: biased sampling …
Persistent link: https://www.econbiz.de/10009278168
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Asymptotic Theory for Multiple-Sample Semiparametric Density Ratio Models and Its Application to Mortality Forecasting
Lu, Guanhua - 2007
A multiple-sample semiparametric density ratio model, which is equivalent to a generalized logistic regression model, can be constructedby multiplicative exponential distortions of a reference distribution. Distortion functions are assumed to be nonnegative and of a known finite-dimensional...
Persistent link: https://www.econbiz.de/10009450972
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Sequential-analysis-based randomized-regret-methods for lotsizing and scheduling
Drexl, Andreas; Haase, Knut - 1993
biased sampling via randomized regrets is presented. We partition the parameter space of the stochastic algorithm and choose …
Persistent link: https://www.econbiz.de/10011613728
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Sequential-analysis-based randomized-regret-methods for lotsizing and scheduling
Drexl, Andreas; Haase, Knut - 1993
biased sampling via randomized regrets is presented. We partition the parameter space of the stochastic algorithm and choose …
Persistent link: https://www.econbiz.de/10011739121
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