搜索结果: 1-15 共查到“理论统计学 maximum likelihood”相关记录26条 . 查询时间(0.015 秒)
Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
font style='font-size:12px;'>
2016/1/25
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...
Relative Performance of Expected and Observed Fisher Information in Covariance Estimation for Maximum Likelihood Estimates
Relative Performance Expected and Observed Fisher Information Covariance Estimation Maximum Likelihood Estimates
font style='font-size:12px;'>
2013/6/13
Maximum likelihood estimation is a popular method in statistical inference. As a way of assessing the accuracy of the maximum likelihood estimate (MLE), the calculation of the covariance matrix of the...
Maximum Likelihood Estimation in Network Models
beta model polytope of degree sequences random graphs Rasch model p1 model
font style='font-size:12px;'>
2011/6/20
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...
Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model
maximum-likelihood Stochastic Block Model
font style='font-size:12px;'>
2011/6/17
The stochastic block model (SBM) is a probabilistic model de-
signed to describe heterogeneous directed and undirected graphs. In this
paper, we address the asymptotic inference on SBM by use of max...
Asymptotic properties of maximum likelihood estimators in models with multiple change points
change-point fraction common parameter consistency convergence rate Kullback–Leibler distance within-segment parameter
font style='font-size:12px;'>
2011/3/24
Models with multiple change points are used in many fields; however, the theoretical properties of maximum likelihood estimators of such models have received relatively little attention. The goal of t...
Smoothed log-concave maximum likelihood estimation with applications
Classification Functional estimation Log-concave maximum likelihood estimation Log-concavity Smoothing
font style='font-size:12px;'>
2011/3/18
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...
Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs
Machine Learning (stat.ML) Neural and Evolutionary Computing (cs.NE)
font style='font-size:12px;'>
2010/12/17
Restricted Boltzmann Machines (RBM) have attracted a lot of attention of late, as one the principle building blocks of deep networks. Training RBMs remains problematic however, because of the intracti...
Geometry of maximum likelihood estimation in Gaussian graphical models
Statistics Theory (math.ST) Algebraic Geometry (math.AG) Optimization and Control (math.OC)
font style='font-size:12px;'>
2010/12/17
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...
Penalized maximum likelihood estimation for generalized linear point processes
Penalized maximum likelihood estimation generalized linear point processes
font style='font-size:12px;'>
2010/3/11
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 ...
Non-Gaussian Quasi Maximum Likelihood Estimation of GARCH Models
Non-Gaussian Quasi Maximum Likelihood Estimation GARCH Models
font style='font-size:12px;'>
2010/3/9
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...
Skewness of maximum likelihood estimators in dispersion models
dispersion models nonlinear models skewness maximum likelihood
font style='font-size:12px;'>
2010/3/9
We introduce the dispersion models with a regression structure to extend the
generalized linear models, the exponential family nonlinear models (Cordeiro and
Paula, 1989) and the proper dispersion m...
Uniqueness of the maximum likelihood estimator for k-monotone densities
uniqueness k–monotone density mixture mod-els density estimation maximum likelihood nonparametric estimation shape constraints
font style='font-size:12px;'>
2010/3/9
We prove uniqueness of the maximum likelihood estimator for
the class of k?monotone densities.
AMS 2000 subject classifications: Primary 62G07.
On approximate pseudo-maximum likelihood estimation for LARCH-processes
asymptotic distribution LARCH process long-range dependence parametricestimation volatility
font style='font-size:12px;'>
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
font style='font-size:12px;'>
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...
Correction to:“Blind maximum likelihood separation of a linear-quadratic mixture”
Correction Blind maximum likelihood separation linear-quadratic mixture
font style='font-size:12px;'>
2010/3/9
An error occurred in the computation of a gradient in [1]. The equa-
tions (20) in Appendix and (17) in the text were not correct. The current
paper presents the correct version of these equations.