Quasi-likelihood and Optimal Estimation

By V. P. Godambe, Chris C. Heyde. 2010

149 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c139ba33695e0c0d190ee0eb974d740a4ba0728f

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In Finance

1. Konstantinos Fokianos
2. Michael SØrensen
3. Bo Martin Bibby
4. Martin Jacobsen
5. V. Christou
6. Christian H. Weiβ
7. David Ruppert
8. Chenlei Leng

In Statistics and Probability

1. Cheng Yong Tang
2. V. Christou
3. Christian H. Weiβ
4. David Ruppert
5. Martin Jacobsen
6. Chenlei Leng
7. Trivellore E. Raghunathan
8. Yun Bai

Citations in the corpus

• QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS (2013, methodology): “This methodology avoids complicated likelihood function, and it ensures that the regression parameters are estimated consistently since we only require the correct mean specification; see Zeger and Liang (1986), Godambe and Heyde (1987) and Heyde (1997).”
• Penalized empirical likelihood and growing dimensional general estimating equations (2012, methodology): “From Theorem 2, the asymptotic varianceV (θ0) of θ̂E remains optimal as in fixed dimensional cases (Hansen, 1982; Godambe & Heyde, 1987).”
• The INARCH(1) Model for Overdispersed Time Series of Counts (2010, result): “1, the CLS estimators have the advantage of being computed more easily (explicit formula instead of numerical maximization) and of having an explicit, data-independent expression for the asymptotic distribution, while the ML estimators usually have smaller standard errors within a class of estimatin”
• CHAPTER 4 – Estimating Functions for Discretely Sampled Diffusion-Type Models (2010, methodology): “This is the basic property of the invariant measure expressed in terms of the generator, a fact noted and used to construct estimating functions by Hansen & Scheinkman (1995), see also Kessler (2000), Baddeley (2000), and Äıt-Sahalia, Hansen & Scheinkman (2003).”
• Joint composite estimating functions in spatiotemporal models (2012, background): “Or we may choose such p and q as to maximize the Godambe information (Godambe and Heyde, 1987) of the corresponding composite estimating functions, so that the resulting estimator will have minimum variance estimates (Bevilacqua et al., 2011).”
• Tapered Covariance: Bayesian Estimation and Asymptotics (2012, background): “…estimating equations, quasi-likelihood, and other areas, and is referred to by various names, including the sandwich matrix, the Godambe information criterion, and the robust information criterion (e.g., Durbin 1960; Bhapkar 1972; Morton 1981; Ferreira 1982; Godambe and Heyde 1987; Heyde 1997).”