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Maximum likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatial-autoregressive disturbances
spreg spatial-autoregressive models
2015/9/24
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...
Least-Squares Covariance Matrix Adjustment
matrix nearness problems covariance matrix least-squares
2015/7/10
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...
Grid-Based Simulation and the Method of Conditional Least Squares
Grid-Based Simulation Method Conditional Least Squares
2015/7/8
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...
Algorithm 937: MINRES-QLP for Symmetric and Hermitian Linear Equations and Least-Squares Problems
Symmetric Hermitian Linear
2015/7/3
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...
Cholesky-based Methods for Sparse Least Squares: The Beneˉts of Regularization
Sparse Least Squares Regularization
2015/7/3
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...
LEAST SQUARES ESTIMATION OF DISCRETE LINEAR DYNAMIC SYSTEMS USING ORTHOGONAL TRANSFORMATIONS
DISCRETE LINEAR DYNAMIC SYSTEMS ORTHOGONAL TRANSFORMATIONS
2015/7/3
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...
A general approach of least squares estimation and optimal filtering
Least squares Optimal filtering Matched filter Noise Optimization Power Spectrum Density
2013/6/17
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...
A least-squares method for sparse low rank approximation of multivariate functions
least-squares method sparse low rank approximation multivariate functions
2013/6/14
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...
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Efficient Regularized；Least-Squares；Algorithms；Conditional Ranking；Relational Data
2012/11/23
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...
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Ridge Regression dimensional subspace
2011/6/16
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...
A Generalized Least Squares Matrix Decomposition
matrix decomposition,singular value decomposition,transposable data,principal components analysis,sparse principal components analysis,functional prin-cipal components analysis,spatio-temporal data
2011/3/21
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...
A Generalized Least Squares Matrix Decomposition
matrix decomposition singular value decomposition transposable data principal components analysis, sparse principal components analysis functional prin-cipal components analysis spatio-temporal data
2011/3/23
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 Degrees of Freedom of Partial Least Squares Regression
regression model selection Partial Least Squares Degrees of Freedom
2010/3/10
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 ...
Kernel Partial Least Squares is Universally Consistent
Kernel Partial Least Squares Universally Consistent
2010/3/18
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...