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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...
Fast Linear Iterations for Distributed Averaging
Distributed consensus Linear system Spectral radius Graph Laplacian Semide!nite programming
2015/7/10
We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nod...
Averaging for Solitons with Nonlinearity Management
Average method of nonlinear schrodinger equation the soliton periodic variation nonlinear coefficient and condensate
2014/12/29
We develop an averaging method for solitons of the nonlinear Schrödinger equation with a periodically varying nonlinearity coefficient, which is used to effectively describe solitons in Bose-Eins...
Averaging of nonlinearity management with dissipation
Optics atomic physics nonlinear partial differential equation the dynamic model of the nonlinear schrodinger
2014/12/24
Motivated by recent experiments in optics and atomic physics, we derive an averaged nonlinear partial differential equation describing the dynamics of the complex field in a nonlinear Schrödinger...
Central limit theorems for pre-averaging covariance estimators under endogenous sampling times
Central limit theorem Hitting times Market microstructure noise Nonsynchronous observa-tions Pre-averaging Time endogeneity
2013/6/13
We consider two continuous It\^o semimartingales observed with noise and sampled at stopping times in a nonsynchronous manner. In this article we establish a central limit theorem for the pre-averaged...
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...
Mirror averaging with sparsity priors
Mirror averaging progressive mixture sparsity aggregation of estimators oracleinequalities
2010/3/11
We consider the problem of aggregating the elements of a (possibly infinite) dictionary
for building a decision procedure, that aims at minimizing a given criterion. Along with the
dictionary, an in...
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...
Nonparametric Bayesian model selection and averaging
Adaptation rate of convergence Bayes factor rate of contraction
2009/9/16
We consider nonparametric Bayesian estimation of a probability density p based on a random sample of size n from this density using a hierarchical prior. The prior consists, for instance, of prior wei...
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...