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Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
Group descent algorithms nonconvex penalized linear model logistic regression model grouped predictors
2012/11/22
Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not indi...
Estimating a Signal from a Magnitude Spectrogram via Convex Optimization
Estimating a Signal Magnitude Spectrogram Convex Optimization
2012/11/22
The problem of recovering a signal from the magnitude of its short-time Fourier transform (STFT) is a longstanding one in audio signal processing. Existing approaches rely on heuristics that often per...
Level set estimation from projection measurements: Performance guarantees and fast computation
estimation projection measurements Performance guarantees fast computation
2012/11/22
Estimation of the level set of a function (i.e., regions where the function exceeds some value) is an important problem with applications in digital elevation mapping, medical imaging, astronomy, etc....
A practical recipe to fit discrete power-law distributions
practical recipe discrete power-law distributions
2012/11/22
Power laws pervade statistical physics and complex systems, but, traditionally, researchers in these fields have paid little attention to properly fit these distributions. Who has not seen (or even sh...
Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models
Univariate and multivariatestable distributions MCMC Approximate,Aayesian,Computation Characteristic function
2012/11/21
In this paper we consider a variety of procedures for numerical statistical inference in the family of univariate and multivariate stable distributions. In connection with univariate distributions (i)...
Augment-and-Conquer Negative Binomial Processes
Augment-and-Conquer Negative Binomial Processes
2012/11/22
By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under the NB process framework. We develop fundamental p...
Marginal Likelihood Computation for Hidden Markov Models via Generalized Two-Filter Smoothing
MarginalLikelihood Sequential MonteCarlo Generalized Two-Filter Smoothing
2012/11/21
In this note we introduce an estimate for the marginal likelihood associated to hidden Markov models (HMMs) using sequential Monte Carlo (SMC) approximations of the generalized two-filter smoothing de...
Hypersurfaces and their singularities in partial correlation testing
Hypersurfaces singularities partial correlation testing
2012/11/21
An asymptotic theory is developed for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations. We study these volu...
Asymptotic properties of robust complex covariance matrix estimates
Asymptotic properties robust complex covariance matrix estimates
2012/11/22
In many statistical signal processing applications, the estimation of nuisance parameters and parameters of interest is strongly linked to the resulting performance. Generally, these applications deal...
In this paper we describe a statistical procedure to account for differences in grading practices from one course to another. The goal is to define a course "inflatedness" and a student "aptitude" tha...
Fitting directed acyclic graphs with latent nodes as finite mixtures models, with application to education transmission
Extendedlatentclassmodels mixturemodels structuralequations causal inference
2012/11/22
This paper describes an efficient EM algorithm for maximum likelihood estimation of a system of nonlinear structural equations corresponding to a directed acyclic graph model that can contain an arbit...
Bayesian variable selection for spatially dependent generalized linear models
generalized linear models variable selection Bayesian spatially
2012/11/22
Despite the abundance of methods for variable selection and accommodating spatial structure in regression models, there is little precedent for incorporating spatial dependence in covariate inclusion ...
Nonparametric estimation in hidden Markov models
Nonparametric estimation hidden Markov models
2012/11/22
This paper outlines a new procedure to perform nonparametric estimation in hidden Markov models. It is assumed that a Markov chain (Xk) is observed only through a process (Yk), where Yk is a noisy obs...
The bivariate current status model
bivariate current status bivariate interval censoring maximum likelihood estimators maximum smoothed likelihood estimators cuberoot nestimation asymptotic distribution
2012/11/22
For the univariate current status and, more generally, the interval censoring model, distribution theory has been developed for the maximum likelihood estimator (MLE) and smoothed maximum likelihood e...
Bayesian inference for nonlinear structural time series models
DSGEmodel Multi-modal Partially adapted particle flter State space
2012/11/21
This article discusses a partially adapted particle filter for estimating the likelihood of a nonlinear structural econometric state space models whose state transition density cannot be expressed in ...