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We describe the spreg command, which implements a maximum likelihood estimator and a generalized spatial two-stage least-squares estimator for the parameters of a linear cross-sectional spatial-auto...
We consider the problem of finding the smallest adjustment to a given symmetric n by n matrix, as measured by the Euclidean or Frobenius norm, so that it satisfies some given linear equalities and ine...
This paper is concerned with the use of simulation to compute the conditional expectations that arise in the method of conditional least squares. Our approach involves performing simulations at each p...
If the system is singular, MINRES-QLP computes the unique minimum-length solution (also known as the pseudoinverse solution), which generally eludes MINRES. In all cases, it overcomes a potential in...
We study the use of black-box LDL T factorizations for solving the augmented systems (KKT systems) associated with least-squares problems and barrier methods for linear programming (LP). With judi...
Kalman [9] introduced a method for estimating the state of a discrete linear dynamic system subject to noise. His method is fast but has poor numerical properties. Duncan and Horn [3] showed that th...
The least squares method allows fitting parameters of a mathematical model from experimental data. This article proposes a general approach of this method. After introducing the method and giving a fo...
In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regul...
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular ta...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component...
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
We consider the problem of robustly predicting as well as the best linear combination of d given functions in least squares regression, and variants of this problem including constraints on the param...
The derivation of statistical properties for Partial Least Squares regression can be a challenging task. The reason is that the construction of latent compo- nents from the predictor variables also ...
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a re- producing kernel Hilbert space. Partial Least Squares stands...

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