搜索结果: 1-15 共查到“统计核算理论 Models”相关记录23条 . 查询时间(0.234 秒)
Learning the Structure of Mixed Graphical Models
Learning the Structure Mixed Graphical Models
2015/8/21
We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and di...
Approximation of epidemic models by diffusion processes and their statistical inference
Approximation epidemic models diffusion processes their statistical inference
2013/6/14
Among various mathematical frameworks, multidimensional continuous-time Markov jump processes $(Z_t)$ on $\N^d$ form a natural set-up for modeling $SIR$-like epidemics. In this study we extend the res...
In this note the relation between the range-renewal speed and entropy for i.i.d. models is discussed.
A General Bernstein--von Mises Theorem in semiparametric models
A General Bernstein von Mises Theorem semiparametric models
2013/6/14
A Bernstein-von Mises theorem is derived for general semiparametric functionals. The result is applied to a variety of semiparametric problems, in i.i.d. and non-i.i.d. situations. In particular, new ...
Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
Efficient Algorithms Multivariate Linear Mixed Models Genome-wide Association Studies
2013/6/17
Multivariate linear mixed models (mvLMMs) have been widely used in many areas of genetics, and have attracted considerable recent interest in genome-wide association studies (GWASs). However, existing...
Sparse approximations in spatio-temporal point-process models
latent Gaussian models linear dynamical systems log Gaussian Cox process approximate inference expectation propagation sparse inference
2013/6/14
Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling gener...
Bayesian Functional Generalized Additive Models with Sparsely Observed Covariates
auction data functional data analysis functional regression linear mixed models measurement error MCMC penalized splines variational inference
2013/6/14
The functional generalized additive model (FGAM) was recently proposed in McLean et al. (2012) as a more flexible alternative to the common functional linear model (FLM) for regressing a scalar on fun...
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
Hierarchically-coupled hidden Markov models learning kinetic rates single-molecule data
2013/6/14
We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measure...
Calibration diagnostics for point process models via the probability integral transform
Calibration diagnostics point process models probability integral transform
2013/6/14
We propose the use of the probability integral transform (PIT) for model validation in point process models. The simple PIT diagnostics assess the calibration of the model and can detect inconsistenci...
Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
Comparison nonhomogeneous regression models probabilistic wind speed forecasting
2013/6/14
In weather forecasting, nonhomogeneous regression is used to statistically postprocess forecast ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regressi...
MCMC for non-linear state space models using ensembles of latent sequences
MCMC non-linear state space models ensembles latent sequences
2013/6/13
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no str...
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods
Observation Driven Time Series Models Approximate Bayesian Computation Asymptotic Con-sistency Markov Chain Monte Carlo
2013/4/28
In the following article we consider approximate Bayesian parameter inference for observation driven time series models. Such statistical models appear in a wide variety of applications, including eco...
Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates
American Community Survey Bayesian hierarchical modeling GoogleTrends Spatial statistics Stochastic search variable selection
2013/4/28
The Fay-Herriot (FH) model is widely used in small area estimation and uses auxiliary information to reduce estimation variance at undersampled locations. We extend the type of covariate information u...
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Distributed Learning Gaussian Graphical Models Marginal Likelihoods
2013/4/28
We consider distributed estimation of the inverse covariance matrix, also called the concentration matrix, in Gaussian graphical models. Traditional centralized estimation often requires iterative and...
Additive inverse regression models with convolution-type operators
Inverse regression Additive models Convolution-type operators Mathematical subject codes: primary 62G08 secondary 62G15 62G20
2013/4/27
In a recent paper Birke and Bissantz (2008) considered the problem of nonparametric estimation in inverse regression models with convolution-type operators. For multivariate predictors nonparametric m...