Parameter Orthogonality and Bias Adjustment for Estimating Functions
By Bent Jørgensen, S. Knudsen. 2004
85 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e8425cacc5d1ff592e8483063b820adb9585acd3
How researchers see this paper
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In Statistics and Probability
- Wagner Hugo Bonat
- Célestin C. Kokonendji
- John P. Hinde
- Clarice G. B. Dem'etrio
- Bent Jørgensen
- Giampiero M. Gallo
- Renjun Ma
- Robert F. Engle
Citations in the corpus
- Simplex Mixed‐Effects Models for Longitudinal Proportional Data (2008, background): “This covariance is marginally affected by the estimation of the variance parameter , because the left-hand side of (9), as an estimating function, is insensitive, namely the expectation of its first-order derivative with respect to is zero (Jørgensen & Knudsen, 2004).”
- Local asymptotic normality for normal inverse Gaussian Lévy processes with high-frequency sampling (2010, background): “, Cox and Reid [8] and Jørgensen and Knudsen [10]).”
- Flexible Tweedie regression models for continuous data (2016, methodology): “In particular, we discuss the quasi-likelihood [10,11] and pseudolikelihood [12] approaches.”
- Vector Multiplicative Error Models: Representation and Inference (2006, methodology): “…as known or unknown; 3. simplification of the estimation algorithm; 4. θ̂(Σ), the estimate of θ when Σ is given, varies only slowly with Σ. Starting from this point, Jørgensen and Knudsen (2004) extend F-orthogonality to EFs introducing the concepts of nuisance parameter insensitivity (henceforth…”
- Multivariate covariance generalized linear models (2015, methodology): “Jørgensen and Knudsen (2004) proposed a bias-correction for the Pearson estimating function.”
- Nested generalized linear mixed models: an orthodox best linear unbiased predictor approach (2007, methodology): “To obtain the asymptotic variance forβ under this expanded parameter space, we employ the results of Jørgensen and Knudsen (2004) on estimating equations with the nuisance parameter insensitive property.”
- Extended Poisson–Tweedie: Properties and regression models for count data (2016, methodology): “Following Jørgensen and Knudsen (2004) and Bonat and Jørgensen (2016), the Pearson estimating function for the dispersion parameters has the following form: ( ,ˇ) = ( − n∑ i=1 ∂C−1i ∂ [ (Yi − i)2 − Ci ] ,− n∑ i=1 ∂C−1i ∂p [ (Yi − i)2 − Ci ]) .”