搜索结果: 1-12 共查到“统计预测理论 Models”相关记录12条 . 查询时间(0.252 秒)
Estimation of Spatial Panel Data Models with Time Varying Spatial Weights Matrices
Spatial autoregression Panel data Time varying spatial weights matrices Fixed e¤ects Maximum likelihood Impact analysis
2016/1/20
This paper investigates the quasi-maximum likelihood (QML) estimation of spatial panel data models where spatial weights matrices can be time varying. We show that QML estimate is consistent and asymp...
Combining Dynamic Predictions from Joint Models for Longitudinal and Time-to-Event Data using Bayesian Model Averaging
Prognostic Modeling Risk Prediction
2013/4/27
The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. More recently, a new and attractive applica...
Application of Predictive Model Selection to Coupled Models
Predictive Model Selection Quantity of In-terest Model Validation Decision Making
2011/7/19
A predictive Bayesian model selection approach is presented to discriminate coupled models used to predict an unobserved quantity of interest (QoI).
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.
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...
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.
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...
Empirical process of residuals for regression models with long memory errors
Empirical process of residuals regression models
2011/3/24
We consider the residual empirical process in random design regression with long memory errors. We establish its limiting behaviour, showing that its rates of convergence are different from the rates ...
Estimating and forecasting partially linear models with non stationary exogeneous variables
-mixing additive models backtting electricity consumption forecasting interval semipara-metric regression smoothing
2011/3/24
This paper presents a backfitting-type method for estimating and forecasting a periodically correlated partially linear model with exogeneous variables and heteroskedastic input noise. A rate of conve...
Estimating and forecasting partially linear models with non stationary exogeneous variables
-mixing additive models backfitting electricity consumption forecasting interval semipara-metric regression smoothing
2011/3/23
This paper presents a backfitting-type method for estimating and forecasting a periodically correlated partially linear model with exogeneous variables and heteroskedastic input noise. A rate of conve...
Compatibility of Prior Specifications Across Linear Models
Bayes factor,compatible prior,conjugate prior,g-prior,hypothesis testing,Kullback–Leibler projection,nested model,variable selection
2011/3/23
Bayesian model comparison requires the specification of a prior distribution on the parameter space of each candidate model. In this connection two concerns arise: on the one hand the elicitation task...
Semiparametric Latent Variable Models for Guided Representation
Variable Models Semiparametric Latent Guided
2011/3/18
Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for learning features re...