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Testing Additive Separability of Error Term in Nonparametric Structural Models
Additive Separability Hypotheses Testing Nonparametric Structural Equation Non- separable Models
2016/1/25
This paper considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model non-additively.We...
Testing Additive Separability of Error Term in Nonparametric Structural Models
Additive Separability Hypotheses Testing Nonparametric Structural Equation
2016/1/20
This paper considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model non-additively.We...
Exploiting Structural Complexity for Robust and Rapid Hyperspectral Imaging
Hyperspectral imaging de-noising Limited an-gle tomography low-rank recovery
2013/6/14
This paper presents several strategies for spectral de-noising of hyperspectral images and hypercube reconstruction from a limited number of tomographic measurements. In particular we show that the no...
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
Structural Functional Discovery Dynamic Networks Non-negative Matrix Factorization
2013/6/17
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix ...
Inference and testing for structural change in time series of counts model
time series of counts Poisson autoregression likelihood estimation change-point semi-parametric test
2013/6/14
We consider here together the inference questions and the change-point problem in Poisson autoregressions (see Tj{\o}stheim, 2012). The conditional mean (or intensity) of the process is involved as a ...
Modeling US house prices by spatial dynamic structural equation models
house prices Bayesian inference dynamic factor models spatio-temporal models cointegration lattice data
2013/4/27
This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the us...
Causal Inference on Time Series using Structural Equation Models
Causal Inference Time Series Structural Equation Models
2012/9/19
Causal inference uses observations to infer the causal structure of the data generating system.We study a class of functional models that we call Time Series Models with Independent Noise (TiMINo). Th...
Modelling outliers and structural breaks in dynamic linear models with a novel use of a heavy tailed prior for the variances: An alternative to the Inverted Gamma
Modelling outliers structural breaks Inverted Gamma
2011/7/19
In this paper we propose a new wider class of hypergeometric heavy tailed priors that are given as the convolution of a Student-t density for the location parameter and a Scaled Beta2 prior for the va...
Metamodel-based importance sampling for structural reliability analysis
reliability analysis importance sampling metamodeling error kriging random fields active learning rare events
2011/6/16
Structural reliability methods aim at computing the probability of failure of systems with
respect to some prescribed performance functions. In modern engineering such functions
usually resort to ru...
Chi-square Intervals for a Poisson Parameter - Bayes, Classical and Structural
Confidence interval coverage probability estimation interval Poisson
2011/3/18
The 'standard' confidence interval for a Poisson parameter is only one of a number of estimation intervals based on the chi-square distribution that may be used in the estimation of the mean or mean r...
Global identifiability of linear structural equation models
Covariance matrix Gaussian distribution graphical model multivari-ate normal distribution parameter identification structural equation model
2010/3/11
Structural equation models are multivariate statistical models that
are defined by specifying noisy functional relationships among random vari-
ables. We consider the classical case of linear relati...
Gaussian Process Structural Equation Models with Latent Variables
Gaussian Process Structural Equation Models Latent Variables
2010/3/11
In a variety of disciplines such as social sciences,
psychology, medicine and economics, the
recorded data are considered to be noisy measurements
of latent variables connected by some
causal stru...
A martingale approach to continuous time marginal structural models
martingale approach continuous time marginal structural models
2010/3/17
Marginal structural models were introduced in order to provide
estimates of causal effects from interventions based on observational studies
in epidemiological research. We present a variant of the ...
A Bayesian Structural Equations Model for Multilevel Data with Missing Responses and Missing Covariates
DIC Latent variable Markov chain Monte Carlo missing at random random eects VHA all employee survey data
2009/9/22
Motivated by a large multilevel survey conducted by the US Veterans
Health Administration (VHA), we propose a structural equations model which in-
volvesa set of latentvariables tocapture dependence...
Dynamic and Structural Features of Intifada Violence: A Markov Process Approach
Bayesian conjugate prior Israeli-Palestinian conict marginal likelihood
2009/9/22
This paper analyzes the daily incidence of violence during the Second
Intifada. We compare several alternative statistical models with dierent dynamic
and structural stability characteristics while ...