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spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
MCMC P-splines spike-and-slab prior normal-inverse-gamma
2011/6/20
The R package spikeSlabGAM implements Bayesian variable selection, model choice,
and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and
Poisson responses. Its purpose ...
Counterfactual actions in graphical models based on local independence
causal inference event history analysis marked point
2011/7/5
We consider a framework for counterfactual statistical analysis with graphical models based on marked point processes. The main idea is to treat the counterfactual scenario as just another probability...
Classification Loss Function for Parameter Ensembles in Bayesian Hierarchical Models
Classification Loss Function Parameter Ensembles Bayesian Hierarchical Models
2011/6/20
Our perspective in this paper follows the framework adopted by Lin et al. (2006), who intro-
duced several loss functions for the identi cation of the elements of a parameter ensemble that
represent...
Maximum Likelihood Estimation in Network Models
beta model polytope of degree sequences random graphs Rasch model p1 model
2011/6/20
We study maximum likelihood estimation for the statistical model for both directed and undirected
random graph models in which the degree sequences areminimal sufficient statistics. In the undirected...
A semiparametric estimation of copula models based on the method of moments
Moments Copulas Dependence Parametric estimation Archimedean copulas
2011/6/20
Using the classical estimation method of moments, we propose a new semiparametric estima-
tion procedure for multi-parameter copula models. Consistency and asymptotic normality of
the obtained estim...
Marginal log-linear parameters for graphical Markov models
multivariate discrete statistical models parametrization marginal log-linear graphical Markov models
2011/6/20
The parametrization of multivariate discrete statistical models by marginal log-linear
(MLL) parameters provides a great deal of flexibility; in particular, different MLL parametrizations
under line...
Calibration and filtering for multi factor commodity models with seasonality: incorporating panel data from futures contracts
Multi-Factor Commodity Spot Price Stochastic Volatility Milstein Adaptive Markov chain Monte Carlo Particle filter Rao-Blackwellization
2011/6/21
We examine a general multi-factor model for commodity spot prices and futures valuation. We extend
the multi-factor long-short model in [1] and [2] in two important aspects: firstly we allow for both...
Lie Markov Models
phylogenetics Lie algebras Lie groups representation theory symmetry Markov chains
2011/6/21
Recent work has discussed the importance of multiplicative closure for the Markov mod-
els used in phylogenetics. For continuous-time Markov chains, a sufficient condition for
multiplicative closure...
Posterior Consistency of Nonparametric Conditional Moment Restricted Models
identified region limited information likelihood sieve approximation nonparametric instrumental variable ill-posed problem partial identification Bayesian inference
2011/6/20
This paper addresses the estimation of the nonparametric conditional moment
restricted model that involves an infinite dimensional parameter g0. We
estimate it in a quasi-Bayesian way based on the l...
State-Observation Sampling and the Econometrics of Learning Models
Hidden Markov model particle filter state-observation sampling learning indirect inference forecasting state space model value at risk
2011/6/20
In nonlinear state-space models, sequential learning about the hidden state can proceed
by particle filtering when the density of the observation conditional on the state is available
analytically (...
High Dimensional Covariance Matrix Estimation in Approximate Factor Models
sparse estimation thresholding cross-sectional correlation common factors idiosyncratic seemingly unrelated regression
2011/6/20
The variance covariance matrix plays a central role in the inferential theories
of high dimensional factor models in finance and economics. Popular
regularization methods of directly exploiting spar...
Corrected portmanteau tests for VAR models with time-varying variance
VAR model Unconditionally heteroscedastic errors Residual autocorrelations Portmanteau tests
2011/6/20
The problem of test of fit for Vector AutoRegressive (VAR) processes
with unconditionally heteroscedastic errors is studied. The volatility structure is
deterministic but time-varying and allows for...
Independent screening for single-index hazard rate models with ultra-high dimensional features
screening univariate regression models generalized linear models single-index
2011/6/17
In data sets with many more features than observations, independent screening based on all
univariate regression models leads to a computationally convenient variable selection method.
Recent effort...
Semiparametric Bivariate Zero-Inflated Poisson Models with Application to Studies of Abundance for Multiple Species
Benthic fish Bivariate Poisson Hierarchical Bayes Missouri River Pspline Zero-inflated Poisson
2011/6/17
Ecological studies involving counts of abundance, presence-absence or occupancy rates
often produce data having a substantial proportion of zeros. Furthermore, these types of
processes are typically...
Local Identification of Nonparametric and Semiparametric Models
Identification Local Identification Nonparametric Models Asset Pricing
2011/6/17
In parametric models a sufficient condition for local identification is that
the vector of moment conditions is differentiable at the true parameter with full rank
derivative matrix. We show that th...