搜索结果: 1-11 共查到“model averaging”相关记录11条 . 查询时间(0.051 秒)
Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/26
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/20
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions
multi-model averaging catchment scale nitrogen predictions
2014/12/17
Hydro-biogeochemical models are used to foresee the impact of mitigation measures on water quality. Usually, scenario-based studies rely on single model applications. This is done in spite of the wide...
Point and interval forecasts of age-specific life expectancies: A model averaging approach
Booth-Maindonald-Smith method functional data analysis Hyndman-Ullah method Lee-Carter method Lee-Miller method principal components analysis random walk with drift
2014/11/21
Background: Any improvement in the forecast accuracy of life expectancy would be beneficial for policy decision regarding the allocation of current and future resources. In this paper, I revisit some ...
Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components
Bayesian model averaging continuous ranked probability score ensemble calibration truncated normal distribution
2013/6/13
Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to ...
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...
Tobit Bayesian Model Averaging and the Determinants of Foreign Direct Investment
Bayesian Model Averaging Foreign Direct Investment Tobit Estimation Gibbs Sampling Conditional Bayes Factors
2012/6/5
We develop a fully Bayesian, computationally efficient framework for incorporating model uncertainty into Type II Tobit models and apply this to the investigation of the determinants of Foreign Direct...
Bayesian Analysis of Comparative Microarray Experiments by Model Averaging
Microarrays gene expression gamma distribution log-normal distribution model averaging true and false positive rates false discovery rate
2009/9/21
A major challenge to the statistical analysis of microarray data is
the small number of samples limited by both cost and sample availability
compared to the large number of genes, now soaring into t...
Bayesian Model Averaging and Bayesian Predictive Information Criterion for Model Selection
Bayesian model averaging Bayesian predictive information criterion Markov chain Monte Carlo
2009/3/5
The problem of evaluating the goodness of the predictive distributions developed by the Bayesian model averaging approach is investigated. Considering the maximization of the posterior mean of the exp...
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
Bayesian analysis Double exponential family Hierarchical priors Variance estimation
2010/4/30
Flexibly modeling the response variance in regression is important for efficient parameter
estimation, correct inference, and for understanding the sources of variability in
the response. Our articl...
The distribution of model averaging estimators and an impossibility result regarding its estimation
model mixing model aggregation combination of estimators model selection finite sample distribution
2010/4/27
The finite-sample as well as the asymptotic distribution of Leung
and Barron’s (2006) model averaging estimator are derived in the context of
a linear regression model. An impossibility result regar...