搜索结果: 151-165 共查到“统计学 I.I.D Models”相关记录456条 . 查询时间(0.297 秒)
Approximate group context tree: applications to dynamic programming and dynamic choice models
categorical time series group context tree
2011/7/19
The paper considers a variable length Markov chain model associated with a group of stationary processes that share the same context tree but potentially different conditional probabilities.
Application of Bayesian model inadequacy criterion for multiple data sets to radial velocity models of exoplanet systems
Methods Statistical – Techniques Radial velocities – Stars Individual: GJ 581 HD 217107 υ Andromedae
2011/7/7
We present a simple mathematical criterion for determining whether a given statistical model does not describe several independent sets of measurements, or data modes, adequately. We derive this crite...
Large information plus noise random matrix models and consistent subspace estimation in large sensor networks
Large information plus noise random matrix models consistent subspace estimation large sensor networks
2011/7/7
In array processing, a common problem is to estimate the angles of arrival of $K$ deterministic sources impinging on an array of $M$ antennas, from $N$ observations of the source signal, corrupted by ...
A Sequence of Relaxations Constraining Hidden Variable Models
Sequence Relaxations Constraining Hidden Variable Models
2011/7/6
Many widely studied graphical models with latent variables lead to nontrivial constraints on the distribution of the observed variables. Inspired by the Bell inequalities in quantum mechanics, we refe...
Distributional Results for Thresholding Estimators in High-Dimensional Gaussian Regression Models
Markov chain Monte Carlo Hamiltonian dynamics Bayesian analysis
2011/7/6
We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n and may diverge with ...
High Dimensional Sparse Econometric Models: An Introduction
High Dimensional Sparse Econometric Models An Introduction
2011/7/6
In this chapter we discuss conceptually high dimensional sparse econometric models as well as estimation of these models using L1-penalization and post-L1-penalization methods.
Parametric inference and forecasting in continuously invertible volatility models
Invertibility volatility models parametric estimation
2011/7/6
We introduce the notion of continuously invertible volatility models that relies on some Lyapunov condition and some regularity condition.
High-dimensional additive hazard models and the Lasso
Survival analysis Counting processes Censored data
2011/7/6
We consider a general high-dimensional additive hazard model in a non-asymptotic setting, including regression for censored-data.
Finite mixture models with predictive recursion marginal likelihood
Density estimation Dirichlet distribution mixture com-plexity
2011/7/6
Estimation of finite mixture models when the mixing distribution support is unknown is an important and challenging problem. In this paper, a new approach is given based on the recently proposed predi...
Stochastic Search for Semiparametric Linear Regression Models
Stochastic Search Semiparametric Linear Regression Models
2011/7/6
This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar (1987).
Semiparametric inference in mixture models with predictive recursion marginal likelihood
Density estimation Dirichlet process mixture empirical Bayes filtering algorithm
2011/7/5
Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm...
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies
Bayesian variable selection generalized linear models Gaussian processes
2011/7/5
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data.
Copula representation of bivariate L-moments : A new estimation method for multiparameter 2-dimentional copula models
Copulas Dependence Multivariate L-moments Parametric estimation
2011/7/5
Recently, Serfling and Xiao (2007) extended the L-moment theory (Hosking, 1990) to the multivariate setting.
A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity.
Spatial wavelet Markov models are more efficient than covariance tapering and process convolutions
Matérn covariances Kriging Wavelets Markov random fields Covariance tapering
2011/7/5
The Mat\'ern covariance function is a popular choice for modeling dependence in spatial environmental data.