Hart, Jeffrey D. Some automated methods of smoothing time-dependent data. (English) Zbl 0878.62031 J. Nonparametric Stat. 6, No. 2-3, 115-142 (1996). Summary: Nonparametric function estimation based upon time-dependent data is a challenging problem to both the data analyst and the theoretician. This paper serves as an introduction to the problem and discusses some of the approaches that have been proposed for smoothing autocorrelated data. A principal theme will be accounting for correlation in the data-driven choice of a function estimator’s smoothing parameter. Data-driven smoothing is considered in various settings, including probability density estimation, repeated measures data, and time series trend estimation. Both applications and theoretical issues are addressed, and some open problems will be discussed. Cited in 23 Documents MSC: 62G07 Density estimation 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) 65C99 Probabilistic methods, stochastic differential equations Keywords:kernel estimators; mean integrated squared error; cross-validation; plug-in rules; autoregression; transition densities; blockwise cross-validation; prequential analysis; time series cross-validation PDFBibTeX XMLCite \textit{J. D. Hart}, J. Nonparametric Stat. 6, No. 2--3, 115--142 (1996; Zbl 0878.62031) Full Text: DOI References: [1] DOI: 10.1007/BF02480283 · Zbl 0445.62053 [2] DOI: 10.2307/2290011 [3] DOI: 10.1016/0304-4149(93)90096-M · Zbl 0780.62031 [4] Banon G. 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