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An Inconsistent Maximum Likelihood Estimate [PDF]
Journal of the American Statistical Association, 1982 Abstract An example is given of a family of distributions on [— 1, 1] with a continuous one-dimensional parameterization that joins the triangular distribution (when Θ = 0) to the uniform (when Θ = 1), for which the maximum likelihood estimates exist and converge strongly to Θ = 1 as the sample size tends to infinity, whatever be the true value of the ...
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Maximum-likelihood estimation of the entropy of an attractor
Physical Review E, 1994In this paper, a maximum-likelihood estimate of the (Kolmogorov) entropy of an attractor is proposed that can be obtained directly from a time series. Also, the relative standard deviation of the entropy estimate is derived; it is dependent on the entropy and on the number of samples used in the estimation.
Schouten, J.C. (author) +2 more
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'Bias reduction of maximum likelihood estimates'
Biometrika, 1993SUMMARY It is shown how, in regular parametric problems, the first-order term is removed from the asymptotic bias of maximum likelihood estimates by a suitable modification of the score function. In exponential families with canonical parameterization the effect is to penalize the likelihood by the Jeffreys invariant prior. In binomial logistic models,
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1997
In the last chapter attention was given to the determination of the state vector1 ξ for given observations Y and known parameters A. In this chapter the maximum likelihood estimation of the parameters \(\lambda = (\theta \prime ,\rho \prime ,\xi {\prime _0})\prime \) of an MS-VAR model is considered.
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In the last chapter attention was given to the determination of the state vector1 ξ for given observations Y and known parameters A. In this chapter the maximum likelihood estimation of the parameters \(\lambda = (\theta \prime ,\rho \prime ,\xi {\prime _0})\prime \) of an MS-VAR model is considered.
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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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On maximum likelihood estimation of a Pareto mixture
Computational Statistics, 2011In this paper we deal with maximum likelihood estimation (MLE) of the parameters of a Pareto mixture. Standard MLE procedures are difficult to apply in this setup, because the distributions of the observations do not have common support. We study the properties of the estimators under different hypotheses; in particular, we show that, when all the ...
Bee, Marco +2 more
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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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Maximum‐likelihood‐estimation stacking [PDF]
SEG Technical Program Expanded Abstracts 2007, 2007 Common-midpoint (CMP) stacking is often done using the arithmetic mean. There are good reasons for this: it is simple, linear, and is optimal if the noise has a Gaussian distribution. Where the noise is not Gaussian, however, the mean does a poor job, resulting in a stacked section contaminated by erratic noise.
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1995
In this chapter, basic properties of maximum likelihood estimators are derived and a useful generalization is obtained. Only results for general finite spaces X are presented. Parameter estimation for Gibbs fields is discussed in the next chapter.
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In this chapter, basic properties of maximum likelihood estimators are derived and a useful generalization is obtained. Only results for general finite spaces X are presented. Parameter estimation for Gibbs fields is discussed in the next chapter.
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Maximum Likelihood Estimation of Misspecified Models
Econometrica, 1982This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest.
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