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• W4385065207 abstract "The transition density of the diffusion process plays an important role in calculating the dynamic characteristics of the underlying variables as well as the model estimation. In this article, we combine the idea of conditional probability density function with non parametric kernel regression, and introduce the kernel estimation of joint density function and marginal density function, then construct the non parametric kernel estimator of the transition density of second-order diffusion process based on discrete observational samples. In order to obtain the asymptotic properties of the new kernel estimator, we analyze the asymptotic expectation and asymptotic variance of the proposed estimator under some mild conditions. Finally, the consistency and asymptotic normality of the new proposed non parametric estimator of the transition density function are proved." @default.
• W4385065207 created "2023-07-23" @default.
• W4385065207 creator A5002567067 @default.
• W4385065207 creator A5041574734 @default.
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• W4385065207 date "2023-07-21" @default.
• W4385065207 modified "2023-10-15" @default.
• W4385065207 title "Non parametric estimation of transition density for second-order diffusion processes" @default.
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• W4385065207 doi "https://doi.org/10.1080/03610926.2023.2234521" @default.
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