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Conditional least squares estimation in nonstationary nonlinear stochastic regression models
Stochastic nonlinear regression heteroscedasticity nonstation-ary process time series branching process conditional least squares estimator quasi-likelihood estimator
2010/3/9
National Agronomical Research Institute (INRA)
Let {Zn} be a real nonstationary stochastic process such that
E(Zn|Fn−1)a.s.< 1 and E(Z2n |Fn−1)a.s.< 1, where {Fn} is an increas-
ing seq...
Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations
Ordinary differential equations parameters estimation profiling procedure consistency asymptotic normality
2010/3/18
Ordinary differential equations (ODEs) are commonly used to
model dynamic behavior of a system. Because many parameters are
unknown and have to be estimated from the observed data, there
is growing...
Spectral estimation of the fractional order of a Lévy process
Regular L´ evy processes Blumenthal–Getoor index semiparametric estimation
2010/3/9
We consider the problem of estimating the fractional order of a
L´evy process from low frequency historical and options data. An estimation
methodology is developed which allows us to treat bo...
On approximate pseudo-maximum likelihood estimation for LARCH-processes
asymptotic distribution LARCH process long-range dependence parametricestimation volatility
2010/3/9
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...
Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model
Current status data maximum smoothed likelihood smoothedmaximum likelihood distribution estimation density estimation hazard rate estimation
2010/3/9
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...
Rates of convergence for the posterior distributions of mixtures of Betas and adaptive nonparametric estimation of the density
Bayesian nonparametric rates of convergence mixtures of Betas adaptive estimation kernel
2010/3/9
In this paper, we investigate the asymptotic properties of nonparametric
Bayesian mixtures of Betas for estimating a smooth density
on [0, 1]. We consider a parametrization of Beta distributions in
...
Tree Density Estimation
kernel density estimation tree structured Markov network high dimensional inference risk consistency structure selection consistency
2010/3/9
We study graph estimation and density estimation in high dimensions. To
avoid the curse of dimensionality, we consider a family of density estimators based on
tree structured undirected graphical mo...
Outlier detection and trimmed estimation in general functional spaces
Abstract Inference Data depth Outlier detection Robust statistics Stochastic processes
2010/3/9
This article introduces trimmed estimators for the mean and covariance functional
of data in general Hilbert spaces. The estimators are based on a data depth measure
that can be computed on any Hilb...
Efficient estimation of copula-based semiparametric Markov models
Copula geometric ergodicity nonlinear Markov models semi-parametric efficiency sieve likelihood ratio statistics sieve MLE tail dependence value-at-risk
2010/3/17
paper considers the efficient estimation of copula-based semi-
parametric strictly stationary Markov models. These models are char-
acterized by nonparametric invariant (one-dimensional marginal) di...
Minima of convex integral functionals and unbiased estimation
convex integral functionals unbiased estimation
2009/9/24
Minima of convex integral functionals and unbiased estimation。
Best unbiased linear estimation, a coordinate free approach
Best unbiased linear estimation a coordinate free approach
2009/9/24
Best unbiased linear estimation, a coordinate free approach。
On sequential estimation of parameters of continuous Gaussian Markov processes
sequential estimation of parameters continuous Gaussian Markov processes
2009/9/24
On sequential estimation of parameters of continuous Gaussian Markov processes。
Sequential estimation for the spectral density parameter of a stationary Gaussian process
Sequential estimation for the spectral density parameter a stationary Gaussian process
2009/9/24
Sequential estimation for the spectral density parameter of a stationary Gaussian process。
Robust estimation and finite population。
Nonparametric estimation of the distribution function in contingent valuation models
binary choice regression Dirichlet process latent variable mixture model variable selection
2009/9/24
Contingent valuation models are used in Economics to value nonmarket goods and can be expressed as binary choice regression models with one of the regression coe?cients fixed. A ...