linear least-squares estimation
Communication efficient distributed weighted non linear least squares estimation
... agents are interconnected over a generic network, and each agent acquires its local measurements in a streaming fashion. These estimators are iterative (recursive), where at each iteration (time instance), each agent ...
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Source term estimation of pollution from an instantaneous point source
... The approach taken was to develop the inverse model as a non-linear least squares estimation problem in which the source term was estimated using pollution concentration measurements on [r] ...
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Least squares estimation of a shift in linear processes
... the least squares method (LS) to the estimation of a shift ...The least squares procedure also allows a broader specification of correlation structure in the data than MLE can typically ...
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Estimation of groundwater flow parameters using least squares
... We investigated two distinct types of conductivity eld. The rst is a smooth, Gaussian like bell shaped function, while the second is a ledge, with a discontinuity running at an angle from one side of the domain to the ...
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Estimation and inference in unstable nonlinear least squares models
... Our method can be a powerful tool for empirical macroeconomic modeling. Our empirical illustration shows how to test for breaks in the context of nonlin- ear models such as the ones used for modeling the federal funds ...
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Ordinary Least Squares Estimation of a Dynamic Game Model
... the estimation of structural dynamic discrete choice ...familiar linear regression techniques, which arise when the period payo¤ functions are modeled to have fully or partially linear-in-parameter ...
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From Equivalent Linear Equations to Gauss Markov Theorem
... reduces linear unbiased estimation to the Least Squares Solution of inconsistent linear equations while the normal equations reduce the second one to the usual solution of consistent ...
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A Least-squares Approach to Direct Importance Estimation
... LSIF is shown to be efficient in computation, but it tends to share a common weakness of reg- ularization path tracking algorithms, that is, accumulation of numerical errors (Scheinberg, 2006). The numerical problem ...
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Kinematic calibration of a 6 DOF hybrid robot by considering multicollinearity in the identification Jacobian
... parameter estimation and pose error compensation is then proposed: (1) coarse estimation and compensation of the encoder offsets using linear least squares; and (2) fine ...
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No penalty no tears : least squares in high dimensional linear models
... sparse linear mod- els by reconsidering OLS and answering the following simple question: Can ordinary least squares consistently fit these models with some suitable algorithms? Our result provides an ...
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Particle swarm optimization and least squares estimation of NARMAX
... types, linear and nonlinear model however in real life, all systems are in nonlinear form ...parameter estimation to enhance the target function where it is involving the difference between the identified ...
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Partially linear models
... partially linear regression smoothing in ...partially linear regression ...partially linear regression techniques to various statistical ...include least squares regression, ...
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glm-ie: Generalised Linear Models Inference & Estimation Toolbox
... To facilitate the specification and composition of system matrices X, B (and their respective trans- poses) in a GLM, the glm-ie toolbox contains a specialised matrix class mat. As MVMs form the most important ...
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Performance of LTE Channel Estimation Algorithms for Different Interpolation Methods and Modulation Schemes
... channel estimation techniques such as Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE) for different interpolation methods and modulation ...environment, linear ...
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A Method for Assessing Customer Harmonic Emission Level Based on the Iterative Algorithm for Least Square Estimation
... on linear regression analysis of basic equation and improvement equation, deduced the least squares estimation (LSE) iterative algorithm and obtained the real-time estimates of regres- sion ...
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Probabilistic partial least squares model: Identifiability, estimation and application
... For estimation of θ, maximum likelihood is ...a least squares type solution if the latent variables t and u are observed, as the model for x and y in (1) involves known t and ...
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Estimation and Inference in Unstable Nonlinear Least Squares Models (Final)
... shows that the parameter that quantifies the response of the interest rate to fluctuations in the discount rate changes at the same time the Fed’s operating procedures change, confirming Lu- cas’ argument. Another ...
164
Least squares estimation of hydraulic conductivity from field data
... piecewise linear functions are used for the boundary ...conductivity estimation, we use total variation constraints in the least squares t-to-data ...
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New Evidence on Linear Regression and Treatment Effect Heterogeneity
... So how can we solve the problem described in Theorem 1? Actually, there are many well-known estimation methods which do not pose similar problems. First, it is suffi- cient to interact the binary variable of ...
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Linear least squares localization in sensor networks
... LLS estimation is a sub-optimum localization algorithm ...LLS estimation usually has a reasonable positioning accuracy and lower implementation complexity, which is essential to many wireless applications, ...
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