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Восстановление зависимости методом наименьших квадратов на основе непараметрической модели с периодической составляющей

Восстановление зависимости методом наименьших квадратов на основе непараметрической модели с периодической составляющей

Abstract

We consider the nonparametric problem of reneval dependence, which is described by the sum of a linear trend and periodic function with a known period. We obtain the asymptotic distribution of the parameter estimates and the trend component. The methods of estimating the periodic component and designing interval forecast. In the model of the points of observation, natural for applications, justified by the conditions of use. In particular, we prove an asymptotically unbiased estimate of the coefficient of the linear term

Рассмотрена непараметрическая задача восстановления зависимости, которая описывается суммой линейного тренда и периодической функции с известным периодом. Получены асимптотические распределения оценок параметров и трендовой составляющей. Разработаны методы оценивания периодической компоненты и построения интервального прогноза. В рамках модели точек наблюдения, естественной для приложений, обоснованы условия применимости. В частности, установлена асимптотическая несмещенность оценки коэффициента линейного члена

Keywords

МЕТОД НАИМЕНЬШИХ КВАДРАТОВ, НЕПАРАМЕТРИЧЕСКИЕ МЕТОДЫ, ПЕРИОДИЧЕСКАЯ СОСТАВЛЯЮЩАЯ, ОЦЕНИВАНИЕ, ПРОГНОЗИРОВАНИЕ, 519.2:303.732.4

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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