Least squares models with a single x-variable
Robust Least Squares Dummy Variable Estimation Of Dynamic Panel Models In The Presence Of Outliers
... exogenous variable and T is period as in [4]. In a system of linear models, the test is not feasible in exactly identified model but rather in over identified models where it is expected that the ...
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Two-stage least squares and indirect least squares algorithms for simultaneous equations models
... The experiments have two objectives. One is to compare algorithms 2SLS QR , 2SLS G and ILS for different sizes in order to determine the most efficient algorithm in each case and compare the costs obtained experimentally ...
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THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
... 1 One can imagine a situation when the values of the predictor variable x i had been ”carefully prepared” prior to measurement, i.e. any errors connected with them are negligible. On the other hand, the y i ...
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Complete Least Squares: A New Variable Screening and Selection Method.
... the variable selection procedure based on the CLS forward addition sequence was ease of ...improved variable selection method, we implement the methods described in Section ...
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A Partial Least Squares based algorithm for parsimonious variable selection
... for variable selection using Partial Least Squares, where the focus is to obtain a hard, and at the same time stable, selection of ...the models with optimum performance based on ...based ...
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Estimation and inference in unstable nonlinear least squares models
... nonlinear least squares (NLS) and features a limited number of parameter shifts occur- ring at unknown ...smooth-transition models in the con- text of an asymmetric US federal funds rate reaction ...
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Least Squares Estimation in Multiple Change-Point Models
... Change-point analysis is a field of mathematical statistics, which concerns itself with the detection and estimation of structural changes within a data set of time-ordered observa- tions. To reach this target, ...
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Variable annuity economic capital: the least-squares Monte Carlo approach
... atory variable seems to be far less significant in the regression than in the one-year projection, but there does appear to be a slight decrease in future liabilities, as the measure of future interest rates ...
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Least squares splines with variable knots using a smoothing spline basis
... (Received 7 August 2000) Abstract A method for constructing a least squares spline with variable knots using a smoothing spline basis is presented. The inherent sta- bility problems of the usual ...
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L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
... linear models, support recovery can be achieved by fitting these models with penalized optimization procedures such as LASSO (Tibshirani, 1996) or Dantzig Selector (Candes and Tao, 2007), which are ...
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USING SEASONAL AND CYCLICAL COMPONENTS IN LEAST SQUARES FORECASTING MODELS
... forecasting models acquire increased accuracy for out-of-sample ...of least squares forecasting models with time series ...in least squares equations does not frequent the ...
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No penalty no tears : least squares in high dimensional linear models
... Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample ...involving least squares ...
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Nonparametric least squares estimation in integer-valued GARCH models
... the variable ( λ n+k , Y n+k ) depends on ( λn , Y n ...the variable Y n+k depend on Y n ? Or even more general, how do the count variables {Y n+k , Y n+k+1 , ...
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Estimation and Inference in Unstable Nonlinear Least Squares Models (Final)
... Chapter 9 Conclusions We considered a univariate NLS models with multiple parameter changes that occur at un- known dates. If the number of breaks is known, we showed that, similar to the linear model of Bai and ...
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Weighted Majorization Algorithms for Weighted Least Squares Decomposition Models
... many least-squares decomposition models efficient algorithms are well ...decomposition models where each residual is weighted by a nonnegative ...decomposition models by iterative ...
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Bounded-variable least-squares methods for linear and nonlinear model predictive control
... We infer from Figure 4.7 that near-perfect reference tracking was ach- ieved with a small offset because of model mismatch, which occurs as the reference is not at the operating point. Moreover, the input references were ...
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Asymptotic properties of weighted least squares estimation in weak parma models
... PARMA models with strong and weak noises were used to investigate the size and power of a Wald test based on a consistent estimator of the asymptotic covariance matrix, under the assumption of either a weak or ...
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Least Squares after Model Selection in High-Dimensional Sparse Models
... ordinary least squares (ols) to the model selected by first-step penal- ized estimators, typically ...at least as well as lasso in terms of the rate of convergence, and has the advantage of a smaller ...
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Keywords Sugarcane, lignin, stalk, partial least squares regression, PLS, variable selection
... final models were all chosen using the algorithm ...predictive models. All the models showed good predictive capability and the methods are inexpensive, environmentally friendly, and extremely ...all ...
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Variable forgetting factor mechanisms for diffusion recursive least squares algorithm in sensor networks
... This paper is organized as follows. Section 2 provides the system model for the distributed estimation over sen- sor networks. Besides, the DRLS algorithm with the fixed forgetting factor is described briefly. In Section ...
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