Log-linear Regressions with Combinations of Variables
Instrumental variables estimation of stationary and nonstationary cointegrating regressions
... Spurious regression was mentioned in the preceding section. This was orig- inally identi…ed as an issue in case of regression between independent I(1) processes. More recently, Cappuccio and Lubian (1997), Tsay and Chung ...
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Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
... on log-transformed variables in order to achieve the principal assumptions of a linear regression ...possible combinations of log-transformed independent or dependent ...two ...
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Geometry of Uncertainty Relations for Linear Combinations of Position and Momentum
... continuous variables, analogous in form to the one originally discovered by ...continuous variables while in finite-dimensional Hilbert spaces numerous investigations of entropic inequalities for multiple ...
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"Minimax Estimation of Linear Combinations of Restricted Location Parameters"
... contradicts that ˆ θ GB dominates ˆ θ U . Hence, if ˆ θ GB dominates ˆ θ U , then a 1 a 2 ≤ 0. 3.3 Non-minimaxity for k ≥ 3 We here treat the case of k ≥ 3 where the setup of the random variables X 1 , . . . , X k ...
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Log linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post Editing
... In NMT ensembles, homogeneous models are typically weighted equally. Here we combine dif- ferent models and equal weighting does not work. Instead, we treat each ensemble component as a feature in a traditional ...
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Order selection in finite mixtures of linear regressions
... explanatory variables does not seem to affect the better selection methods on the other hand whereas it has a substantial detrimental effect on the estimators of the relative or symmetric Kullback-Leibler ...
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Detection of Collinearity Effects on Explanatory Variables and Error Terms in Multiple Regressions
... classical linear regression model and the overall values of R 2 and adjusted value of R 2 is always quantify how well the response variable is been ...explanatory variables will produce the necessary ...
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Selected Problems for High-Dimensional Data - Quantile and Errors-in-Variables Regressions.
... regular regression and have positive components over the all time points. To the ex- tent that the coefficient patterns found when using measurement error regression are interpreted as being constant, this is consistent ...
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Predictive regressions based on managerial decision variables: Is there a small-sample bias?
... r mt = a + u t , E t = c + d E t−1 + v t , with b = cov(r mt , E t−1 ) var(E t−1 ) , where r is the aggregate market return, either equal- or value-weighted, and E is one of two measures of equity issues. The equity ...
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A New Control Function Approach for Non-Parametric Regressions with Endogenous Variables
... the linear simultaneous equations models are numerically ...the linear setting, the non-linear and non-parametric control function setting of Newey, Powell, and Vella (1999) (NPVCF) is no longer ...
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The Role of the Log Transformation in Forecasting Economic Variables
... economic variables in Section ...to linear forecasts are given for sample sizes of T = ...the log transformation to y t may not result in sizable improvements in the homogeneity of the ...
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Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation
... geological variables and geo- spatial location of the samplings across Mariana Trench, which proves the interplay between multiple factors affecting its geo- ...
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Recognition by Linear Combinations of Models
... Methods using this approach assume that the set of possible transformations is restricted (usually to rigid 3-D transformations with possible scaling or a Lie tra[r] ...
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LINEAR COMBINATIONS OF HYPONORMAL OPERATORS
... But, by hypothesis, each Tkj is hyponormal and so T^Tkj—TkjT^ > 0.. Note that the proof of the preceding proposition also shows that each Tij is normal since Akk = 0 for all k and ea[r] ...
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Weak Dependence on Initialization in Mixture of Linear Regressions
... of Linear Regressions Ryohei Nakano and Seiya Satoh Abstract—This paper examined how initial values of a learning algorithm influence the performance of mixture of linear regressions ...
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Learning non-linear combinations of kernels
... finite linear combinations of base kernels as originally considered by ...such linear combinations of kernels, no method seems to re- liably give improvements over baseline ...learned ...
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Special linear combinations of orthogonal polynomials
... special linear combinations of polynomials, orthogonal with the weight p(x) on the interval (a, ...special linear combinations of Chebyshev orthogonal polynomials of four kinds with absolutely ...
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Linear Combinations of Heuristics for Examination Timetabling
... using linear combinations of heuristics for weighted graphs which model the timetabling problems under ...heuristic combinations define specific roles that each simple heuristic contributes to the ...
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Aggregation of log-linear risks
... THOMAS MIKOSCH, ∗∗∗ University of Copenhagen, Denmark Abstract In this paper we work in the framework of a k-dimensional vector of log- linear risks. Under weak conditions on the marginal tails and the ...
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Log-Linear Description Logics
... Under the given syntax and semantics the first central infer- ence task is the maximum a-posteriori (MAP) query: “Given a EL ++ - LL CBox, what is a most probable coherent EL ++ CBox over[r] ...
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