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This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. A...
A solution method and an estimation method for nonlinear rational expectations models are presented in this paper. The solution method can be used in forecasting and policy applications and can hand...
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
We study maximum likelihood estimation for the statistical model for both directed and undirected random graph models in which the degree sequences areminimal sufficient statistics. In the undirected...
We study the smoothed log-concave maximum likelihood estimator of a probability distribution on $\mathbb{R}^d$. This is a fully automatic nonparametric density estimator, obtained as a canonical smoot...
We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. An algebraic elimination criterion allows us to find exact lower bounds on the number of observation...
A framework of generalized linear point process models (glppm) much akin to glm for regression is developed where the intensity depends upon a linear predictor process through a known function.In the ...
The non-Gaussian quasi maximum likelihood estimator is frequently used in GARCH models with intension to improve the efficiency of the GARCH parameters. However, the method is usually inconsistent u...
Linear ARCH (LARCH) processes were introduced by Robinson [J. Econometrics 47 (1991) 67–84] to model long-range dependence in volatility and leverage. Basic theoretical properties of LARCH processes...
We consider the problem of estimating the distribution function, the density and the hazard rate of the (unobservable) event time in the current status model. A well studied and natural nonparametri...
Improved Maximum Likelihood Estimation in a New Class of Beta Regression Models

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