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Pi Wen Tsai

Researcher at National Taiwan Normal University

Publications -  19
Citations -  334

Pi Wen Tsai is an academic researcher from National Taiwan Normal University. The author has contributed to research in topics: Fractional factorial design & Optimal design. The author has an hindex of 10, co-authored 19 publications receiving 299 citations. Previous affiliations of Pi Wen Tsai include University of Reading & National Health Research Institutes.

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Rejecting strictly allopatric speciation on a continental island: prolonged postdivergence gene flow between Taiwan (Leucodioptron taewanus, Passeriformes Timaliidae) and Chinese (L. canorum canorum) hwameis

TL;DR: The findings imply that the role of geographical barrier may be less prominent for the speciation of continental island endemics, and suggest a shift in speciation studies from simply correlating geographical barrier and genetic divergence to examining factors that facilitate and maintain divergence, e.g. differential selection and sexual selection.
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Projective three-level main effects designs robust to model uncertainty

TL;DR: In this article, a columnwise design procedure is used to construct three-level designs for six factors in 18 runs and the projection efficiencies of these designs are explored and compared with designs obtained from the L 18 orthogonal array.
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Optimal two-level regular fractional factorial block and split-plot designs

TL;DR: In this paper, a general and unified approach to the selection of regular fractional factorial designs, which can be applied to experiments that are unblocked, blocked or have a split-plot structure, is proposed.
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Testing Founder Effect Speciation: Divergence Population Genetics of the Spoonbills Platalea regia and Pl. minor (Threskiornithidae, Aves)

TL;DR: The potential importance of evolutionarily labile traits with significant fitness consequences, such as migratory behavior and habitat preference, in facilitating divergence of the spoonbills is discussed.
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A General Criterion for Factorial Designs Under Model Uncertainty

TL;DR: In this paper, the QB criterion, which aims to improve the estimation in as many models as possible by incorporating experimenters' prior knowledge along with an approximation to the As criterion, is presented.