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- W2079562898 abstract "Consider an ergodic Markov chain on the real line, with parametric models for the conditional mean and variance of the transition distribution. Such a setting is an instance of a quasi-likelihood model. The customary estimator for the parameter is the maximum quasi-likelihood estimator. It is not efficient, but as good as the best estimator that ignores the parametric model for the conditional variance. We construct two efficient estimators. One is a convex combination of solutions of two estimating equations, the other a weighted nonlinear one-step least squares estimator, with weights involving predictors for the third and fourth centered conditional moments of the transition distribution. Additional restrictions on the model can lead to further improvement. We illustrate this with an autoregressive model whose error variance is related to the autoregression parameter." @default.
- W2079562898 created "2016-06-24" @default.
- W2079562898 creator A5061347821 @default.
- W2079562898 date "1996-02-01" @default.
- W2079562898 modified "2023-10-17" @default.
- W2079562898 title "Quasi-likelihood models and optimal inference" @default.
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- W2079562898 doi "https://doi.org/10.1214/aos/1033066217" @default.
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