Abstract:We consider statistical inference for a class of dynamic mixed-effect models described by stochastic differential equations whose drift and diffusion coefficients simultaneously depend on fixed- and random-effect parameters. Assuming that each process is observed at high frequency and the number of individuals goes to infinity, we propose a stepwise inference procedure and prove its theoretical properties. The methodology is based on suitable quasi-likelihood functions by profiling the random effect in the diffusion coefficient at the first stage, and then integrating out the Gaussian random effect in the drift coefficient to obtain the marginal distribution in the second stage, resulting in a fully explicit and computationally convenient method. It is also the strength of the proposed approach that the proposed method allows a wide variety of distributions for the random effects in the diffusion coefficient.
From: Maud Delattre [view email]
[v1]
Mon, 25 Aug 2025 11:29:09 UTC (653 KB)
[v2]
Sat, 27 Dec 2025 14:05:31 UTC (1,843 KB)
[v3]
Sat, 22 Aug 2026 20:18:36 UTC (1,901 KB)
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