length. Finally, we compare our new method to both an existing method (BEST 2.2) with similar goals and the supermatrix (concatenation) method. We demonstrate that both BEST and our method have much better estimation accuracy for species tree topology than concatenation, and our method outperforms BEST

of vintage year dummies): Log fund size and log fund sequence number, each of which is included in levels and squares. Our results are reported in 22 Consistent with Kaplan and Schoar, we find only weak evidence that higher sequence number funds perform better (p=0.099) once we exclude fund size in column

Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a

of fMRI responses. In this work we compare several techniques that vary in their assumptions, model complexity, and interpretation. For each method, we introduce methods for estimating amplitude, peak latency, and duration and for performing inference in a multi-subject fMRI setting. We then assess

Public reporting burden for this collection of Information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send

The goal of this paper is to characterize the best achievable performance for the problem of estimating an unknown parameter having a sparse representation. Specifically, we consider the setting in which a sparsely representable deterministic parameter vector is to be estimated from measurements

. In particular, the Randall-Sundrum model is well-known for achieving an amelioration of the hierarchy problem. However, the basic model relies on Minkowski branes and is subject to solar system constraints in the absence of a radion stabilization mechanism. We present a method by which a four-dimensional low

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Abstract—We address the problem of Bayesian estimation where the statistical relation between the signal and measure-ments is only partially known. We propose modeling partial Bayesian knowledge by using an auxiliary random vector called instrument. The statistical relations between the instrument