A penalized basis-expansion approach for multi-predictor functional linear models that estimates predictor lags by grid search minimizing prediction error and examines theoretical properties through simulations.
On trend and its derivatives estimation in repeated time series with subordinated long-range dependent errors
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abstract
For temporal regularly spaced datasets, a lot of methods are available and the properties of these methods are extensively investigated. Less research has been performed on irregular temporal datasets subject to measurement error with complex dependence structures, while this type of datasets is widely available. In this paper, the performance of kernel smoother for trend and its derivatives is considered under dependent measurement errors and irregularly spaced sampling scheme. The error processes are assumed to be subordinated Gaussian long memory processes and have varying marginal distributions. The functional central limit theorem for the estimators of trend and its derivatives are derived and bandwidth selection problem is addressed.
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stat.ME 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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On estimation of the effect lag of predictors and prediction in functional linear model
A penalized basis-expansion approach for multi-predictor functional linear models that estimates predictor lags by grid search minimizing prediction error and examines theoretical properties through simulations.