Asymptotic normality of the Parzen–Rosenblatt density estimator for strongly mixing random fields
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DOI: 10.1007/s11203-011-9052-4
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Cited by:
- Younso, Ahmad, 2017. "On the consistency of a new kernel rule for spatially dependent data," Statistics & Probability Letters, Elsevier, vol. 131(C), pages 64-71.
- Wang, Yizao & Woodroofe, Michael, 2014. "On the asymptotic normality of kernel density estimators for causal linear random fields," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 201-213.
- Mohamed El Machkouri, 2013. "On the asymptotic normality of frequency polygons for strongly mixing spatial processes," Statistical Inference for Stochastic Processes, Springer, vol. 16(3), pages 193-206, October.
- Sophie Dabo-Niang & Camille Ternynck & Anne-Françoise Yao, 2016. "Nonparametric prediction of spatial multivariate data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 428-458, June.
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More about this item
Keywords
Central limit theorem; Kernel density estimator; Strongly mixing random fields; Spatial processes; 62G05; 62G07; 60G60;All these keywords.
JEL classification:
Statistics
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