129 citations · 568 across the 49 of their papers we have counts for
14 papers · 1 filter
Tuned Regularized Estimators for Linear Regression via Covariance Fitting
Per Mattsson, Dave Zachariah, Petre Stoica
We consider the problem of finding tuned regularized parameter estimators for linear models. We start by showing that three known optimal linear estimators belong to a wider class…
Effect Inference from Two-Group Data with Sampling Bias
Dave Zachariah, Petre Stoica
In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference betwee…
Model-Robust Counterfactual Prediction Method
Dave Zachariah, Petre Stoica
We develop a novel method for counterfactual analysis based on observational data using prediction intervals for units under different exposures. Unlike methods that target heterog…
Pearson information-based lower bound on Fisher information
Dave Zachariah, Petre Stoica
The Fisher information matrix (FIM) plays an important role in the analysis of parameter inference and system design problems. In a number of cases, however, the statistical data d…
Online Hyperparameter-Free Sparse Estimation Method
Dave Zachariah, Petre Stoica
In this paper we derive an online estimator for sparse parameter vectors which, unlike the LASSO approach, does not require the tuning of any hyperparameters. The algorithm is base…
Cramer-Rao bound analog of Bayes rule
Dave Zachariah, Petre Stoica
In this lecture note, we show a general property of the Cramer-Rao bound (CRB) that quantifies the interdependencies between the parameters in a vector. The presented result is val…
Weighted SPICE: A Unifying Approach for Hyperparameter-Free Sparse Estimation
Petre Stoica, Dave Zachariah, Jian Li
In this paper we present the SPICE approach for sparse parameter estimation in a framework that unifies it with other hyperparameter-free methods, namely LIKES, SLIM and IAA. Speci…
Estimation for the Linear Model with Uncertain Covariance Matrices
Dave Zachariah, Nafiseh Shariati, Mats Bengtsson +2
We derive a maximum a posteriori estimator for the linear observation model, where the signal and noise covariance matrices are both uncertain. The uncertainties are treated probab…
Utilization of Noise-Only Samples in Array Processing With Prior Knowledge
Dave Zachariah, Magnus Jansson, Mats Bengtsson
For array processing, we consider the problem of estimating signals of interest, and their directions of arrival (DOA), in unknown colored noise fields. We develop an estimator tha…
Line Spectrum Estimation with Probabilistic Priors
Dave Zachariah, Petter Wirfält, Magnus Jansson +1
For line spectrum estimation, we derive the maximum a posteriori probability estimator where prior knowledge of frequencies is modeled probabilistically. Since the spectrum is peri…
Self-Localization of Asynchronous Wireless Nodes With Parameter Uncertainties
Dave Zachariah, Alessio De Angelis, Satyam Dwivedi +1
We investigate a wireless network localization scenario in which the need for synchronized nodes is avoided. It consists of a set of fixed anchor nodes transmitting according to a…
Bayesian Estimation with Distance Bounds
Dave Zachariah, Isaac Skog, Magnus Jansson +1
We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayes…
Dynamic Iterative Pursuit
Dave Zachariah, Saikat Chatterjee, Magnus Jansson
For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over tim…
Alternating Least-Squares for Low-Rank Matrix Reconstruction
Dave Zachariah, Martin Sundin, Magnus Jansson +1
For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any…