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In this article we study the estimation of the location of jump points in the first derivative (referred to as kinks) of a regression function μ in two random design models with different long-range...
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 ...
We introduce new quantile estimators with adaptive importance sampling. The adaptive estimators are based on weighted samples that are neither independent nor identically distributed. Using a new l...
Upon reading the paper Efficient Likelihood Estimation in State Space Models by Cheng-Der Fuh I found a number of problems in the formulations and a number of mathematical errors. Together, these fi...
We consider Markov Random Fields defined by finite-region conditional probabilities depending on a neighborhood of the region which changes with the boundary conditions. The formal definition of the...
High-frequency data observed on the prices of financial assets are commonly modeled by diffusion processes with micro-structure noise, and realized volatility-based methods are often used to estimat...
We also investigate methods for the estimation of the precision parameter of the Dirichlet process, finding that maximum likelihood may not be desirable, but a posterior mode is a reasonable approac...
we construct adaptive confidence bands that are honest for all densities in a “generic” subset of the union of t-H¨older balls, 0 < t  r, where r is a fixed but arbitrary integer. The exceptional (...
In this paper, the maximum Lq-likelihood estimator (MLqE), a new parameter estimator based on nonextensive entropy [Kibernetika 3 (1967) 30–35] is introduced. The properties of the MLqE are stud- i...
We consider the problem of estimating the mixing density f from n i.i.d. observations distributed according to amixture density with unknown mixing distribution. In contrast with finite mixtures model...
We propose an `1-penalized estimation procedure for high-dimensional lin- ear mixed-effects models. The models are useful whenever there is a grouping structure among high-dimensional observations, ...
Improved EM strategies, based on the idea of efficient data augmentation (Meng and van Dyk 1997, 1998), are presented for ML estimation of mixture proportions. The resulting algorithms inherit the s...
In this paper we derive lower bounds in minimax sense for estimation of the instantaneous volatility if the diffusion type part cannot be observed directly but under some additional Gaussian noise. ...
Given n noisy samples with p dimensions, where n  p, we show that the multi-step thresholding procedure based on the Lasso – we call it the Thresholded Lasso, can accurately estimate a sparse vector ...
An Markov chain Monte Carlo simulation method based on a two stage delayed rejection Metropolis-Hastings algorithm is proposed to estimate a factor multivariate stochastic volatility model. The firs...

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