Results 221 to 230 of about 208,181 (263)
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Maximum entropy and maximum likelihood in spectral estimation
IEEE Transactions on Information Theory, 1998Summary: The power spectral measure, an informative feature of a stationary time-discrete stochastic process, describes the relative strength of uncorrelated frequency components that compose the process. In spectral estimation one wants to describe the spectral measures of processes having a prescribed initial block of autocorrelation coefficients. In
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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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Maximum Likelihood Estimation of Misspecified Models
Econometrica, 1982zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the Maximum Likelihood Estimator of the Emax Model
It is known in the literature that maximum likelihood esti- mation of the Emax model parameters often encounters computational problems. Our contribution provides a new understanding and control of all the experimental situations. In particular, exact MLE for a three-point experimental design is shown, and we identify the two scenarios where the MLE ...G. Aletti, C. May, C. Tommasi
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Chinese Sociological Review, 2013
Advanced statistical models rely on maximum likelihood (ML) estimators to estimate unknown parameters. Given the complexity and highly technical nature of the numerical approaches embedded in ML, textbooks typically offer oversimplified descriptions of ML, omitting important details from the discussion.
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Advanced statistical models rely on maximum likelihood (ML) estimators to estimate unknown parameters. Given the complexity and highly technical nature of the numerical approaches embedded in ML, textbooks typically offer oversimplified descriptions of ML, omitting important details from the discussion.
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1982
This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
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This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
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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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Estimating unknown parameters in uncertain differential equation by maximum likelihood estimation
Soft Computing, 2022, Liu Baoding
exaly
Generalized Maximum Likelihood Estimators
Theory of Probability & Its Applications, 1966Weiss, L., Wolfowitz, Jacob
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