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1971
It is possible to develop a number of systems of estimation and nowhere does this seem to be more true than for the estimation of genetic crossover fractions. Several of these fail dismally because of inaccuracy and inefficiency (Fisher and Balmukand, 1928).
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It is possible to develop a number of systems of estimation and nowhere does this seem to be more true than for the estimation of genetic crossover fractions. Several of these fail dismally because of inaccuracy and inefficiency (Fisher and Balmukand, 1928).
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Generalized Maximum Likelihood Estimators
Theory of Probability & Its Applications, 1966Weiss, L., Wolfowitz, Jacob
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1996
Let \( \{ ({x'_i},{y_i})\} _{i = 1}^N \) be an iid sample drawn from a known distribution F(x i,y i, s), where s is a k × 1 vector of unknown parameters. Let f y|x (y, β) denote the likelihood function of y | x, which is the density function of y | x if y |x is continuous or the probability of y | x if y | x is discrete.
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Let \( \{ ({x'_i},{y_i})\} _{i = 1}^N \) be an iid sample drawn from a known distribution F(x i,y i, s), where s is a k × 1 vector of unknown parameters. Let f y|x (y, β) denote the likelihood function of y | x, which is the density function of y | x if y |x is continuous or the probability of y | x if y | x is discrete.
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Uncertain maximum likelihood estimation with application to uncertain regression analysis
Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2020Waichon Lio, Baoding Liu
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2002
In this chapter maximum likelihood estimates (MLEs) of the parameters in growth curve models are discussed. Also expectations and variancecovariance matrices of the estimates are considered. In general, the MLE of the regression coefficient is different from the generalized least square estimate (GLSE) discussed in Chapter 2, because the former is a ...
Jian-Xin Pan, Kai-Tai Fang
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In this chapter maximum likelihood estimates (MLEs) of the parameters in growth curve models are discussed. Also expectations and variancecovariance matrices of the estimates are considered. In general, the MLE of the regression coefficient is different from the generalized least square estimate (GLSE) discussed in Chapter 2, because the former is a ...
Jian-Xin Pan, Kai-Tai Fang
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Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
, 1992Tim Bollerslev, J. Wooldridge
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2003
In this chapter, basic properties of estimators are collected. Gibbs fields are examined in the next chapter. Since the product structure of the sample space does not play any role for these considerations, let X be any finite set.
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In this chapter, basic properties of estimators are collected. Gibbs fields are examined in the next chapter. Since the product structure of the sample space does not play any role for these considerations, let X be any finite set.
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Microeconometrics: Maximum Likelihood and Nonlinear Least-Squares Estimation
, 2005A. Cameron, P. Trivedi
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