Practical performance of local likelihood for circular density estimation
| Autor: | Agnese Panzera, Stefania Fensore, M. Di Marzio, Charles C. Taylor |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Normalization (statistics) education.field_of_study Mathematical optimization Estimation theory Applied Mathematics 05 social sciences Population Probability and statistics Density estimation 01 natural sciences Likelihood principle 010104 statistics & probability Sample size determination Modeling and Simulation 0502 economics and business 0101 mathematics Statistics Probability and Uncertainty education Likelihood function Algorithm 050205 econometrics Mathematics |
| Zdroj: | Journal of Statistical Computation and Simulation. 86:2560-2572 |
| ISSN: | 1563-5163 0094-9655 |
| DOI: | 10.1080/00949655.2016.1149588 |
| Popis: | Local likelihood has been mainly developed from an asymptotic point of view, with little attention to finite sample size issues. The present paper provides simulation evidence of how likelihood density estimation practically performs from two points of view. First, we explore the impact of the normalization step of the final estimate, second we show the effectiveness of higher order fits in identifying modes present in the population when small sample sizes are available. We refer to circular data, nevertheless it is easily seen that our findings straightforwardly extend to the Euclidean setting, where they appear to be somehow new. |
| Databáze: | OpenAIRE |
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