This paper concerns detection of the change point, which is treated as an abrupt change in the response function or one of its derivatives. The change point is identified using the semiparametric model and the theory given by Speckman (1994). The theory is illustrated by a real experiment in wchich the dry biomass of winter wheat is studied.
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We propose a class of unbiased and strongly consistent nonparametric kernel estimates of a probability density function, based on a random choice of the sample size and the kernel function. The expected sample size can be arbitrarily small and mild conditions on the local behavior of the density function are imposed.
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