ordinary least squares
Analyzing the share of agriculture and industrial sectors in the economic growth of ethiopia: an ordinary least squares (ols) application
... There are several different frameworks in which the linear regression model can be cast in order to make the OLS technique applicable. Each of these settings produces the same formulas and same results. The only ...
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Using wavelets to obtain a consistent ordinary least squares estimator of the long memory parameter
... an ordinary least squares estimator of the long-memory parameter from a fractionally integrated process that is an alternative to the Geweke and Porter-Hudak (1983) ...consistent ordinary ...
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<p>Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study</p>
... in ordinary least squares (OLS) ITS analysis of cumulative incidence measures, based on prespecified parameters and have developed Stata code to estimate ...
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Comparison of linear regression models Ordinary Lasso, Adaptive Group Lasso and Ordinary Least Squares models in selecting effective characteristics to predict the expected return
... the ordinary least squares method is the simplest and most common ...the ordinary least squares method is to obtain the model coefficients of values that are closest to the ...
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A Risk Comparison of Ordinary Least Squares vs Ridge Regression
... of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a principal component analysis) and then performs an ordinary ...
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High dimensional ordinary least squares projection for screening variables
... Variable selection is a challenging issue in statistical applications when the number of predictors p far exceeds the number of observations n. In this ultra-high dimensional setting, the sure independence screening ...
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Ordinary least squares regression of ordered categorical data: inferential implications for practice
... The interpretation of parameter estimates from this model may be more easily understood by practitioners than those from the probit model. This may be due to the higher frequency of application of this model; however, ...
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ORDINARY LEAST SQUARES METHOD AND ITS VARIANTS
... do one select components to delete and what are consequences of each choice’? Usually the number of PC, which are extracted from the x’s is smaller than the number of the x’s. Some of commonly used criteria suggested for ...
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A comparison of the bounds, beta approximate, and exact variants of 2 tests for heteroscedasticity based on ordinary least squares residuals
... Precis: This paper compares the small sample empirical size, power and incidence of inconclusiveness of the bounds tests for heteroscedasticity proposed by Szroeter 1978 and Harrison and[r] ...
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A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
... In Section 3, we will present our main theoretical results, which consist of bounds for these two statistical quantities for variants of random sampling and random projection sketching algorithms. In particular, we ...
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Ordinary Least Squares Estimation of a Dynamic Game Model
... There are at least two reasons why the estimation of dynamic games can be non-trivial. First, as well-known from the single-agent problem, it involves value functions that generally do not have closed-form and ...
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Consistent least squares fitting of ellipsoids
... The OLS estimation of the ellipsoid parameters from noisy measurements of points on its boundary is a nonlinear least squares problem. An indirect, suboptimal approach was used that transforms the ellipsoid ...
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APPLICATION OF RESTRICTED LEAST SQUARES TO ECONOMETRIC DATA
... restricted least squares estimation, insight can be gained about the behaviour of ordinary least squares estimates and associated tests when the restrictions imposed in the model are ...
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3D Deformation Using Moving Least Squares
... We present a 3d deformation method based on Moving Least Squares that extends the work by Schaefer et al. [Schaefer et al. 2006] to the 3d setting. The user controls the deformation by ma- nipulating a set ...
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Sparse least trimmed squares regression.
... with the smallest squared residuals with respect to the fit in (3.4). The non- sparse FAST-LTS algorithm uses elemental subsets of size p, since any OLS regression requires at least as many observations as the ...
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Theory of Errors and Least Squares Adjustment
... Theory of errors and least squares adjustment is an important subject within the geomatics programme o¤ered at KTH. This is due to the fact that surveying and mapping (or production of spatial data) often ...
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An Introduction to Partial Least Squares Regression
... the first ten PLS factors, for both the factors and the responses. Notice that the first five PLS factors ac- count for almost all of the variation in the responses, with the fifth factor accounting for a sizable ...
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Teaching Least Squares in Matrix Notation
... The least squares method, a fundamental piece of knowledge for students of all scientific tracks, is often introduced considering the simple linear regression with only two parameters to be ...the ...
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Completely Recursive Least Squares and Its Applications
... Most power system state estimation (SE) programs are formulated as static weighted-least-squares (WLS) problems with one-scan data [Monticelli00]. Dynamic state estimation (DSE) is not popularly applied due ...
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GURLS: A Least Squares Library for Supervised Learning
... State of the art results in high-dimensional multi-output problems. Usability and modularity: Easy to use and to expand. GURLS is based on Regularized Least Squares (RLS) and takes advantage of all the ...
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