Results 111 to 120 of about 199,140 (151)
Some of the next articles are maybe not open access.
Generalized Maximum Likelihood Estimators
Theory of Probability & Its Applications, 1966Weiss, L., Wolfowitz, Jacob
openaire +2 more sources
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.
openaire +1 more source
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.
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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.
openaire +2 more sources
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.
openaire +2 more sources