• Weed & Turfgrass Science
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• v.4 no.2
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• pp.85-94
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• 2015

The weed flora in the mulberry fields were investigated in Suwon, Jeonju, and Buan in May, July, and September of 2014. The objectives of this study were to use the survey data for establishing weed control methods and to bring awareness of possible problematic weeds in the Korean mulberry fields. The survey was conducted in 53 regions, covering approximately $145,925m^2$. Altogether 153 weed species of 37 families were identified, of which 68 were annual, 39 species were biennial and 46 were perennial. The dominance was the highest with Digitaria ciliaris followed by Erigeron annuus, Chenopodium album, Echinochloa crus-galli var.crus-galli, Acalypha australis, Commelina communis etc. Exotic weeds presented 44 species with 28.8% of a total presence, of which Erigeron annuus was the highest, followed by Chenopodium album, Phytolacca americana, Conyza canadensis, Oxalis corymbosa etc. Especially, we should aware Senecio vulgaris, not controlled with glufosinate ammonium SL in the Korean mulberry fields because it was known as atrazine resistance in US, Canada, Germany etc. In the PCA plot, weeds presented in the mulberry fields were divided into two groups, Eclipta prostrata community and Stellaria aquatic community and weed flora of Suwon and Buan were different due to those only presented in Suwon.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.