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Testing Additive Separability of Error Term in Nonparametric Structural Models
Additive Separability Hypotheses Testing Nonparametric Structural Equation Non- separable Models
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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
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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...
Accounting for Expectational and Structural Error in Binary Choice Problems: A Moment Inequality Approach
discrete choice models moment inequalities entry models
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2015/7/17
Many economic decisions involve a binary choice - for example, when consumers decide to purchase a good or when rms decide to enter a new market.
In such settings, agents' choices often depend on im...
Structural Characterization of Taboo-Stationarity for General Processes in Two-sided Time
Quasi-stationarity
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2015/7/8
This note considers the taboo counterpart ofstationarity. A general stochastic process in two-sided time is de.ned to be taboo-stationary if its global distribution does not change by shifting the ori...
Closure vs. Structural Holes: How Social Network Information and Culture Affect Choice of Collaborators
Social Network Information Culture
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2015/7/3
Collaboration is important to successful organizations and
how coworkers are selected is crucial to the dynamics of
effective collaborations. In this study we explore how
people use social netwo...
Exploiting Structural Complexity for Robust and Rapid Hyperspectral Imaging
Hyperspectral imaging de-noising Limited an-gle tomography low-rank recovery
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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
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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
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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
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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...
Gender Wage Gaps Reconsidered: A Structural Approach Using Matched Employer-Employee Data
Gender Wage Gaps Structural Approach Matched Employer-Employee Data
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2016/3/9
In this paper, we study the extent to which wage differentials between men and women can be explained by differences in productivity, disparities in friction patterns, segregation, and wage discrimina...
Causal Inference on Time Series using Structural Equation Models
Causal Inference Time Series Structural Equation Models
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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
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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
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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
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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
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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...