搜索结果: 16-30 共查到“管理学 Parameter”相关记录78条 . 查询时间(0.176 秒)
Estimation of the lead-lag parameter from non-synchronous data
contrast estimation discretely observed continuous-time processes Hayashi–Yoshida covariation estimator lead-lag effect
2013/4/28
We propose a simple continuous time model for modeling the lead-lag effect between two financial assets. A two-dimensional process $(X_t,Y_t)$ reproduces a lead-lag effect if, for some time shift $\va...
A parameter estimation method based on random slow manifolds
Parameter estimation Slow-fast system Random slow manifold Quantifying uncer-tainty Numerical optimization
2013/5/2
A parameter estimation method is devised for a slow-fast stochastic dynamical system, where often only the slow component is observable. By using the observations only on the slow component, the syste...
Monitoring procedure for parameter change in causal time series
Sequential change detection Change-point Causal processes Quasi-maximum likelihood estimator Weak convergence.
2012/11/22
We propose a new sequential procedure to detect change in the parameters of a process $ X= (X_t)_{t\in \Z}$ belonging to a large class of causal models (such as AR($\infty$), ARCH($\infty$), TARCH($\i...
Asymptotically efficient estimation of a scale parameter in Gaussian time series and closed-form expressions for the Fisher information
efficient estimation fractional Brownian motion Fisher information general monotone sequence regular variation slowly varying functions spectral density.
2012/9/18
Mimicking the maximum likelihood estimator, we construct first order Cramer-Rao efficient and explicitly computable estimators for the scale parameterσ2 in the model Zi,n =σn−βXi+Yi, i = 1, . . ...
Parameter-Free High-Dimensional Screening Using Multiple Grouping of Variables
Parameter-Free High-Dimensional Screening Multiple Grouping Variables
2012/9/17
Screening is the problem of estimating a superset of the set of non-zero entries in an unknownp-dimensional vector β given nnoisy observations. In the high-dimensional regime, where p > n, screening a...
A note on Bayesian credible sets in restricted parameter space problems and lower bounds for frequentist coverage
Bayesian methods Credible sets Frequentist coverage probability Lower bound Restricted Parameter Spending function
2012/9/17
For estimating a lower bounded parametric function in the framework of Marchand and Strawderman(2006), we provide “through” a unified approach a class of Bayesian confidence intervals with credibility...
Estimation of a nonnegative location parameter with unknown scale
Concave loss Convex loss Dominance Estimation Generalized Bayes Lower bounded mean,Lloss Minimax Restricted parameter Residual vector Robustness.
2012/9/19
For normal canonical models, and more generally a vast arrayof general spherically symmetric location-scale models with a residual vector, we consider estimatingthe (univariate) location parameter whe...
Parameter and Structure Learning in Nested Markov Models
Parameter Structure Learning in Nested Markov Models
2012/9/19
The constraints arising from DAG mod-els with latent variables can be naturally represented by means of acyclic directed mixed graphs (ADMGs). Such graphs contain directed (!) and bidirected ($) arrow...
Parameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods
Parameter estimatio stochastic Morris-Lecar neuronal mode particle filter methods
2012/9/19
In this paper, we consider the classic measurement error regression scenario in which our independent,or design, variables are observed with several sources of additive noise. We will show that our mo...
Dynamics of stochastic non-Newtonian fluids driven by fractional Brownian motion with Hurst parameter $H \in (1/4,1/2)$
fractional Brownian motion stochastic non-Newtonian fluid
2011/7/19
In this paper we consider the Stochastic isothermal, nonlinear, incompressible bipolar viscous fluids driven by a genuine cylindrical fractional Bronwnian motion with Hurst parameter $H \in (1/4,1/2)$...
Classification Loss Function for Parameter Ensembles in Bayesian Hierarchical Models
Classification Loss Function Parameter Ensembles Bayesian Hierarchical Models
2011/6/20
Our perspective in this paper follows the framework adopted by Lin et al. (2006), who intro-
duced several loss functions for the identi cation of the elements of a parameter ensemble that
represent...
Parameter estimation in high dimensional Gaussian distributions
high dimensional Gaussian Parameter estimation massive memory
2011/6/20
In order to compute the log-likelihood for high dimensional spatial Gaussian models, it is
necessary to compute the determinant of the large, sparse, symmetric positive definite precision
matrix, Q....
Hidden Markov Mixture Autoregressive Models: Parameter Estimation
Hidden Markov Model Mixture Autoregressive Model Parameter Estimation
2011/6/17
This report introduces a parsimonious structure for mixture of au-
toregressive models, where the weighting coefficients are determined
through latent random variables as functions of all past obser...
The LASSO for generic design matrices as a function of the relaxation parameter
linear regression LASSO relaxation parameter
2011/6/16
The LASSO is a variable subset selection procedure in statistical
linear regression based on ℓ1 penalization of the least-squares
operator. Its behavior crucially depends, both in practice and...
Wavelet estimation of the long memory parameter for Hermite polynomial of Gaussian processes
Hermite polynomials of a Gaussian process long–memory parameter non–Gaussian Rosenblatt
2011/6/16
We consider stationary processes with long memory which are non–Gaussian and represented
as Hermite polynomials of a Gaussian process. We focus on the corresponding
wavelet coefficients and study th...