Results 251 to 260 of about 175,146 (298)

Quasi-likelihood estimation for relative risk regression models

Biostatistics, 2004
For a prospective randomized clinical trial with two groups, the relative risk can be used as a measure of treatment effect and is directly interpretable as the ratio of success probabilities in the new treatment group versus the placebo group. For a prospective study with many covariates and a binary outcome (success or failure), relative risk ...
Carter, Rickey E., Lipsitz, Stuart R., Tilley, Barbara C.   +2 more
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Asymptotic Properties of the Maximum Quasi-Likelihood Estimator in Quasi-Likelihood Nonlinear Models

Communications in Statistics - Theory and Methods, 2008
Quasi-likelihood nonlinear models (QLNM) are a further extension of generalized linear models by only specifying the expectation and variance functions of the response variable. In this article, some mild regularity conditions are proposed. These regularity conditions, respectively, assure the existence, strong consistency, and the asymptotic normality
Tian Xia, Fanchao Kong, Shunfang Wang, Xueren Wang   +3 more
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On the efficiency of quasi-likelihood estimation

Biometrika, 1987
The introduction of quasi-likelihood for general linear models by \textit{R. W. M. Wedderburn} [Biometrika 61, 439-447 (1974; Zbl 0292.62050)] greatly widened their scope by replacing the full distributional assumption about the random component by the existence of the first and second moments.
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Nonconservative Estimating Functions and Approximate Quasi-Likelihoods

Annals of the Institute of Statistical Mathematics, 1999
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Shrinkage estimation strategy in quasi-likelihood models

Statistics & Probability Letters, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ejaz Ahmed, S., Fallahpour, Saber
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The quasi-likelihood estimation in regression

Annals of the Institute of Statistical Mathematics, 1996
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Optimal estimating functions and wedderburn's quasi-likelihood

Communications in Statistics - Theory and Methods, 1993
There are two inference methods which can be considered as developed from the classical least squares and maximum likelihood methods. One was put forward by Wedderburn (1974) and is called the quasi-likelihood method. Another was introduced by Godambe and others from the viewpoint of the estimating functions. This method is also called quasi-likelihood
Yan-Xia Lin, C.C. Heyde
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Covariance estimation and quasi-likelihood analysis

2019
In modern finance theory, asset prices are typically modeled as a semimartingale. In addition, recent technological developments have made financial high frequency data commonly available. These motivate researchers to develop statistical inference for semimartingales based on high-frequency observation data.
Yuta Koike, Nakahiro Yoshida
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Corrected Estimators in Extended Quasi-Likelihood Models

Communications in Statistics - Theory and Methods, 2008
This paper addresses extended quasi-likelihood models where both the mean and the dispersion parameters vary across observations in a parameterized fashion. We derive formulae for the second-order biases of the maximum quasi-likelihood estimators of all parameters in these models.
Gauss M. Cordeiro, Clarice G. B. Demétrio   +1 more
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Approximate Quasi-likelihood Estimation in Models With Surrogate Predictors

Journal of the American Statistical Association, 1990
Abstract We consider quasi-likelihood estimation with estimated parameters in the variance function when some of the predictors are measured with error. We review and extend four approaches to estimation in this problem, all of them based on small measurement error approximations.
Raymond J. Carroll, Leonard A. Stefanski
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