Results 131 to 140 of about 1,621,703 (196)
Phylogenetic and Molecular Characterization of a Novel Reassortant High-Pathogenicity Avian Influenza A (H7N6) Virus Detected in New Zealand Poultry. [PDF]
Int J Mol SciWilson A +8 more
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The mitochondrial genome of a giant dipluran species, <i>Heterojapyx souliei</i> (Diplura: Japygoidea: Heterojapygidae) from the Tibet Plateau. [PDF]
Mitochondrial DNA B ResourTong CY, Huang CW.
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A statistical inference framework for FSNBLR: Modeling underdeveloped regional status in Eastern Indonesia. [PDF]
MethodsXZulfadhli M +4 more
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Reliability Modeling Method for Constant Stress Accelerated Degradation Based on the Generalized Wiener Process. [PDF]
Entropy (Basel)Li S, Yan Z, Jia J.
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2013
A classical problem in the statistical decision theory is to estimate the probability distribution of a random vector X given its independent observations \(x_{1},\ldots,x_{n}\). Often it is assumed that the probability distribution comes from some family of functions parametrized by a set of parameters \(\theta _{1},\ldots,\theta _{m}\), so that in ...
Michael Zabarankin, Stan Uryasev
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A classical problem in the statistical decision theory is to estimate the probability distribution of a random vector X given its independent observations \(x_{1},\ldots,x_{n}\). Often it is assumed that the probability distribution comes from some family of functions parametrized by a set of parameters \(\theta _{1},\ldots,\theta _{m}\), so that in ...
Michael Zabarankin, Stan Uryasev
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2011
The most popular estimation approach is the maximum likelihood (ML) method. In this chapter, the ML estimator is defined first, and important asymptotic properties of the ML estimator are formulated in Sect. 4.2. Trans- formations of estimators, not only ML estimators, are discussed in Sect. 4.3. To illustrate the ML approach, we consider the ML method
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The most popular estimation approach is the maximum likelihood (ML) method. In this chapter, the ML estimator is defined first, and important asymptotic properties of the ML estimator are formulated in Sect. 4.2. Trans- formations of estimators, not only ML estimators, are discussed in Sect. 4.3. To illustrate the ML approach, we consider the ML method
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2006
Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
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Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
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Maximum likelihood and prediction error methods
Automatica, 1979Abstract The basic ideas behind the parameter estimation methods are discussed in a general setting. The application to estimation or parameters in dynamical systems is treated in detail using the prototype problem of estimating parameters in a continuous time system using discrete time measurements. Computational aspects are discussed.
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Journal of Econometrics, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Broze, Laurence, Gouriéroux, Christian
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Broze, Laurence, Gouriéroux, Christian
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