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.

Practical performance of local likelihood for circular density estimation / Di Marzio, M; Fensore, S.; Panzera, A.; C., C. Taylor. - In: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. - ISSN 0094-9655. - STAMPA. - 86:(2016), pp. 2560-2572. [10.1080/00949655.2016.1149588]

Practical performance of local likelihood for circular density estimation

PANZERA, AGNESE;
2016

Abstract

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.
2016
86
2560
2572
Di Marzio, M; Fensore, S.; Panzera, A.; C., C. Taylor
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1075252
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