Selecting likelihood weights by cross-validation

By Xiaogang Wang, James V. Zidek. 2005

55 citations recorded by the source corpus.

The (relevance) weighted likelihood was introduced to formally embrace a variety of statistical procedures that trade bias for precision. Unlike its classical counterpart, the weighted likelihood combines all relevant information while inheriting many of its desirable features including good asymptotic properties. However, in order to be effective, the weights involved in its construction need to be judiciously chosen. Choosing those weights is the subject of this article in which we demonstrate the use of cross-validation. We prove the resulting weighted likelihood estimator (WLE) to be weakly consistent and asymptotically normal. An application to disease mapping data is demonstrated.

Subfields: Artificial Intelligence, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c75ef82878411c06f4e2a70dcaba60878b7d2821

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kesar Singh
2. Min‐ge Xie
3. William E. Strawderman
4. Jun Yan
5. Lan Wang

In Environmental Engineering

1. Kesar Singh
2. Min‐ge Xie
3. William E. Strawderman
4. Jun Yan

In Statistics and Probability

1. Min‐ge Xie
2. Mekonnen Gebremichael
3. Kesar Singh
4. William E. Strawderman
5. Xiaojing Wang
6. Jun Yan
7. Lan Wang

Citations in the corpus

• Weighted likelihood copula modeling of extreme rainfall events in Connecticut. (2010, background): “As sample sizes at all stations increase, borrowing strength from sites nearby 152 becomes less important, the cross-validation process knows to assign less weight to them 153 (Wang and Zidek, 2005).”
• Confidence Distributions and a Unifying Framework for Meta-Analysis (2011, methodology): “…here are fixed and nonadaptive weights, although under some mild restrictions the development can be extended to include adaptive weights including those used in Section 4.1 or something similar to those used in Hu and Zidek (2002) or Wang and Zidek (2005) in the context of weighted likelihood.”