• The Research Journal of the Costume Culture
• /
• v.9 no.6
• /
• pp.872-880
• /
• 2001

Knockoff products, called forgeries or imitations, are copies of famous original brand goods from abroad. Most of knockoff fashion products manufactured in Korea are famous fashion brand names. The purposes of the paper us: first, to examine and analyze general consumer purchasing behavior toward knockoff fashion products; second, to analyze the purchasing behavior toward knockoff goods according to demographic features; third, to materialize the attributes of knockoff fashion items consumers purchase; fourth, to reveal the relation between the attribute types of knockoff goods, and demographic features and purchasing behavior. The result explained so for reveals that the students'purchasing behaviour differs according to demographic features and general features of the products purchasing. This result should not be interpreted by expanding to the whole group of girl college students or consumers, as the sample used in this study is limited to those in Seoul area. Therefore, following studies are expected to expand the range of subjects'age, and to compare and analyze purchasing behaviour difference of knockoff products and imported original brand names.

• The Korean Journal of Applied Statistics
• /
• v.35 no.6
• /
• pp.685-701
• /
• 2022

FDR is widely used in high-dimensional data inference since it provides more liberal criterion contrary to FWER which is known to be very conservative by controlling Type-1 errors. This paper proposes a sparse VHAR model estimation method controlling FDR by adapting the knockoff introduced by Barber and Candès (2015). We also compare knockoff with conventional method using adaptive Lasso (AL) through extensive simulation study. We observe that AL shows sparsistency and decent forecasting performance, however, AL is not satisfactory in controlling FDR. To be more specific, AL tends to estimate zero coefficients as non-zero coefficients. On the other hand, knockoff controls FDR sufficiently well under desired level, but it finds too sparse model when the sample size is small. However, the knockoff is dramatically improved as sample size increases and the model is getting sparser.