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Identification of universally optimal circular designs for the interference model
Approximate design theory circular design interference model linear equations system universal optimality
2016/1/26
Many applications of block designs exhibit neighbor and edge ef-fects. A popular remedy is to use the circular design coupled with the interference model. The search for optimal or efficient designs h...
Identification of spatial panel Durbin models
Spatial autoregression Durbin regressors Spatial VAR Dynamic panel Fixed e¤ects Random e¤ects
2016/1/26
Identi…cation of a spatial Durbin model is a concern in the spatial econometrics literature. The concern is similar to the identi…cation of the endogenous e¤ect, the contextual e¤ect and correlation e...
Identification of universally optimal circular designs for the interference model
Approximate design theory circular design interference model linear equations system universal optimality
2016/1/20
Many applications of block designs exhibit neighbor and edge ef-fects. A popular remedy is to use the circular design coupled with the interference model. The search for optimal or efficient designs h...
Identification of spatial panel Durbin models
Spatial autoregression Durbin regressors Spatial VAR Dynamic panel Fixed e¤ects Random e¤ects
2016/1/20
Identi…cation of a spatial Durbin model is a concern in the spatial econometrics literature. The concern is similar to the identi…cation of the endogenous e¤ect, the contextual e¤ect and correlation e...
Relaxed Maximum a Posteriori Fault Identification
Fault detection Statistical estimation Convex relaxation Interior-point methods
2015/7/9
We consider the problem of estimating a pattern of faults, represented as a binary vector, from a set of measurements. The measurements can be noise corrupted real values, or quantized versions of noi...
Mixed Linear System Estimation and Identification
Statistical estimation Convex relaxation Interior-point methods
2015/7/9
We consider a mixed linear system model, with both continuous and discrete inputs and outputs, described by a coefficient matrix and a set of noise variances. When the discrete inputs and outputs are ...
Identification of Signal, Noise, and Indistinguishable Subsets in High-Dimensional Data Analysis
Two-Level Thresholding Signal detection False positive control False negative control Multiple testing Variable screening
2013/6/13
Motivated by applications in high-dimensional data analysis where strong signals often stand out easily and weak ones may be indistinguishable from the noise, we develop a statistical framework to pro...
Independent Vector Analysis: Identification Conditions and Performance Bounds
Independent Vector Analysis Identification Conditions Performance Bounds
2013/5/2
Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been the subject of significant research interest. IVA has a...
Best arm identification via Bayesian gap-based exploration
Best arm identification Bayesian gap-based exploration
2013/4/28
Bayesian approaches to optimization under bandit feedback have recently become quite popular in the machine learning community. Methods of this type have been found to have not only very good empirica...
The Identification of Thresholds and Time Delay in Self-Exciting Threshold a Model by Wavelet
threshold autoregressive model threshold time delay wavelet
2013/5/2
In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here...
Linear system identification using stable spline kernels and PLQ penalties
linear system identification bias-variance trade off kernel-based regularization robust statistics interior point methods piecewise linear quadratic densities
2013/4/27
The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model order selection step is...
Automated Bayesian System Identification with NARX Models
Automated Bayesian System Identification NARX Models
2013/5/2
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is...
Identification and well-posedness in nonparametric models with independence conditions
Identification well-posedness nonparametric models independence conditions
2012/11/22
This paper provides a nonparametric analysis for several classes of models, with cases such as classical measurement error, regression with errors in variables, factor models and other models that may...
The Graphical Identification for Total Effects by using Surrogate Variables
Graphical Identification Total Effects Surrogate Variables
2012/9/19
Consider the case where cause-effect relation-ships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model. This paper provides graphical...
Simultaneous SNP identification in association studies with missing data
Hierarchical models Bayes models Gibbs sampling genome-wide association.
2012/9/18
Association testing aims to discover the underlying relationship between genotypes (usually Single Nucleotide Polymorphisms, or SNPs) and phenotypes(attributes, or traits). The typically large data se...