activity
20122021
most citedTesting for threshold effects in the TARMA framework

13 citations · 36 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ME2021★ 13 cited

Testing for threshold effects in the TARMA framework

Greta Goracci, Simone Giannerini, Kung-Sik Chan +1

We present supremum Lagrange Multiplier tests to compare a linear ARMA specification against its threshold ARMA extension. We derive the asymptotic distribution of the test statist…

stat.OT2014

A Conversation with Howell Tong

Kung-Sik Chan, Qiwei Yao

The following conversation is partly based on an interview that took place in the Hong Kong University of Science and Technology in July 2013.

stat.AP2014★ 1 cited

Testing for shielding of special nuclear weapon materials

Kung-Sik Chan, Jinzheng Li, William Eichinger +1

Nuclear-weapon-material detection via gamma-ray sensing is routinely applied, for example, in monitoring cross-border traffic. Natural or deliberate shielding both attenuates and d…

math.ST2012★ 10 cited

Inference of seasonal long-memory aggregate time series

Kung-Sik Chan, Henghsiu Tsai

Time-series data with regular and/or seasonal long-memory are often aggregated before analysis. Often, the aggregation scale is large enough to remove any short-memory components o…

stat.AP2012★ 10 cited

Semiparametric zero-inflated modeling in multi-ethnic study of atherosclerosis (MESA)

Hai Liu, Shuangge Ma, Richard Kronmal +1

We analyze the Agatston score of coronary artery calcium (CAC) from the Multi-Ethnic Study of Atherosclerosis (MESA) using the semiparametric zero-inflated modeling approach, where…

stat.ME2012★ 1 cited

Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong

Kung-Sik Chan, Ruey S. Tsay

Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong [arXiv:1104.3073]

stat.ME2012★ 1 cited

Reduced rank regression via adaptive nuclear norm penalization

Kun Chen, Hongbo Dong, Kung-Sik Chan

Adaptive nuclear-norm penalization is proposed for low-rank matrix approximation, by which we develop a new reduced-rank estimation method for the general high-dimensional multivar…