Name
Title | ||
|---|---|---|
Robust Estimation By Means Of Scaled Bregman Power Distances. Part I. Non-Homogeneous Data |
Abstract | ||
|---|---|---|
In contemporary data analytics, one often models uncertainty-prone data as samples stemming from a sequence of independent random variables whose distributions are non-identical but linked by a common (scalar or multidimensional) parameter. For such a context, we present in the current Part I a new robustness-featured parameter-estimation framework, in terms of minimization of the scaled Bregman power distances of Stummer and Vajda [23] (see also [21]); this leads to a wide range of outlier-robust alternatives to the omnipresent (nonrobust) method of maximum-likelihood-examination, and extends the corresponding method of Ghosh and Basu [7]. In Part II (see [20]), we provide some applications of our framework to data from potentially rare but dangerous events described by approximate extreme value distributions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-26980-7_33 | GEOMETRIC SCIENCE OF INFORMATION |
DocType | Volume | ISSN |
Conference | 11712 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Birgit Roensch | 1 | 0 | 1.01 |
| Wolfgang Stummer | 2 | 2 | 3.50 |