Name
Abstract | ||
|---|---|---|
Representative points (RPs) are a set of points that optimally represents a distribution in terms of mean square error. When the prior data is location biased, the direct methods such as the k-means algorithm may be inefficient to obtain the RPs. In this article, a new indirect algorithm is proposed to search the RPs based on location-biased datasets. Such an algorithm does not constrain the parameter model of the true distribution. The empirical study shows that such algorithm can obtain better RPs than the k-means algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/03610918.2017.1385813 | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
Keywords | Field | DocType |
Good lattice point set,Kernel estimator,Randomized likelihood sampling,Representative point | Econometrics,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
48.0 | 2.0 | 0361-0918 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zong-Feng Qi | 1 | 0 | 1.01 |
| Yong-Dao Zhou | 2 | 0 | 0.68 |
| Kai-Tai Fang | 3 | 165 | 23.65 |