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Controlling for sample selection on the cooperation decision, our estimates show that firms collaborating with competitors and public research organizations (PROs), especially when they are foreign, are more likely to delay or stop an innovation project because of difficulties encountered in their R&D partnerships.
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To handle the case of different data distributions, this method extends the approach from the author's previous work [53] and utilizes a clustering based sample selection method on the latent subspace as to select source data that is most similar to the target data.
We will demonstrate the effects of sample selection bias on the accuracy of ancestry recovery by using experiments on simulated and real data.
Fig. 2 Comparative performance of different sample selection methods on information loss.
The effects of the new sample selection scheme on the recognition performance are shown in Figures 8(b) and 8(c), where the corresponding average rank 1 and rank 5 identification rates are compared, respectively.
Further details containing justification for varying the student characteristic of "prior exposure" to active learning from experiment I to experiment II and how we implemented the sample selection checks on this parameter are given in subsections 5.4, 5.1, and 6.1, respectively.
Figure 1 presents a flowchart of sample selection based on inclusion and exclusion criteria.
Zadrozny (2004) discusses the properties of learning algorithms and the effect of sample selection bias on their accuracy.
When 116,415 SNPs are included in the ADMIXTURE analysis, we observe no effects of sample selection bias on the accuracy of ancestry inference.
5 The former is related to sample selection (conditioning on S) and the latter to analytical decisions with a dataset at hand.
To our knowledge, there has not been a systematic study of the effect of sample selection bias on the accuracy of population structure recovery and individual ancestry recovery.
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