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- W2963116991 abstract "Grouped data are commonly encountered in applications. All data from a continuous population are grouped due to rounding of the individual observations. The Bernstein polynomial model is proposed as an approximate model in this paper for estimating a univariate density function based on grouped data. The coefficients of the Bernstein polynomial, as the mixture proportions of beta distributions, can be estimated using an EM algorithm. The optimal degree of the Bernstein polynomial can be determined using a change-point estimation method. The rate of convergence of the proposed density estimate to the true density is proved to be almost parametric by an acceptance–rejection argument used for generating random numbers. The proposed method is compared with some existing methods in a simulation study and is applied to the Chicken Embryo Data." @default.
- W2963116991 created "2019-07-30" @default.
- W2963116991 creator A5079539570 @default.
- W2963116991 date "2017-09-12" @default.
- W2963116991 modified "2023-09-30" @default.
- W2963116991 title "Bernstein polynomial model for grouped continuous data" @default.
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- W2963116991 doi "https://doi.org/10.1080/10485252.2017.1374384" @default.
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