Adaptive estimation of an ergodic diffusion process based on sampled data

By Masayuki Uchida, Nakahiro Yoshida. 2012

134 citations recorded by the source corpus.

Subfields: Finance, Modeling and Simulation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4823e914eb4c66b1eab49fdd33ffb21620094e86

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In Finance

1. Chenxu Li
2. Hiroki Masuda
3. V. Todorov

In Modeling and Simulation

1. Chenxu Li
2. Hiroki Masuda

In Statistics and Probability

1. Chenxu Li
2. Hiroki Masuda
3. Jian Wang

Citations in the corpus

• Convergence of Gaussian quasi-likelihood random fields for ergodic Lévy driven SDE observed at high frequency (2013, methodology): “Under the present assumptions, we can apply Yoshida [48], Lemma 4, to get E0[|∂kθ ′n(f ; θ)|K ] T −K/2n + T 1−Kn T −K/2 n for k ∈ {0,1} and K ≥ 2, yielding that maxk=0,1 supθ supn E0[| √ Tn∂ k θ × ′n(f ; θ)|K ] < ∞.”
• Maximum-likelihood estimation for diffusion processes via closed-form density expansions (2013, methodology): “(3.15) To guarantee the convergence, our expansion procedure begins with standardizing Xǫ(1) to Y ǫ :=D(x0) Xǫ(1)− x0 ǫ =D(x0) Xǫ(1)− x0√ ∆ ,(3.16) which converges to a nonconstant random variable (a multivariate normal in our case), see Watanabe [65] and Yoshida [67, 68] for a similar setting.”