Other Variables in the Model
Model checking in errors-in-variables regression
... Available online 10 March 2008 Abstract This paper discusses a class of minimum distance tests for fitting a parametric regression model to a class of regression functions in the errors-in-variables ...
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Model selection and model averaging in nonparametric instrumental variables models
... for model uncertainty, we introduce a new model averaging estimator for nonparametric instrumental variables regres- ...than other existing ...
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Model selection and model averaging in nonparametric instrumental variables models
... for model uncertainty, we introduce a new model averaging estimator for nonparametric instrumental variables regres- ...than other existing ...
46
Errors in variables regression: What is the appropriate model?
... in variables problem is th a t it is only tractable if all the distributions describing variation in the d ata are assumed to be ...e model, usually assumptions about the error ...
338
Linear instrumental variables model averaging estimation
... This version: April 2013 Abstract Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates ...
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A model of relevant antecedents and outcome variables for causerelated
... the model show that a brand that is more familiar and important enhances attitudes toward the ...the model did not show a direct effect between cause familiarity and attitude toward the alliance, The Nature ...
13
Consistent estimation in the bilinear multivariate errors in variables model
... The matrix X 0 is the nominal or true value of the parameter. From the point of view of errors-in-variables models, C C ~ represents the equation error, while A A ~ and B B ~ represent the measurement errors. ...
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On a model for the Navier–Stokes equations using magnetization variables
... new model system for the Navier–Stokes equations, obtained from the magnetization variables formulation by the omitting a Leray projector in one of the nonlinear ...
15
Testing and model selection for prediction in large sets of variables
... non-prognostic variables contained in the score are estimated more precisely and, despite of the selection procedure, will tend to be the ones closed to zero with a small contribution ...non-prognostic ...
142
Measurement errors in generalized linear model explanatory variables
... Under the assumption that response and explanatory variables follow a generalized linear model, estimating equations are derived for the case in which the explanatory variables are measu[r] ...
39
Selection of Input Variables for a Thermal Load Prediction Model
... BEMS variables to be assessed. All of the variables are selected based on the sensors already installed at the testbed ...These variables are divided into two categories, input and output ...
6
Theory and Applications of TAR Model with Two Threshold Variables
... threshold variables. Our model is applied to identify the regimes of the Hong Kong stock ...threshold variables, our estimation shows that the stock market of Hong Kong can be classi fi ed into a ...
34
DSGE Model-Based Forecasting of Non-modelled Variables
... DSGE model-based forecast for variables that do not explicitly appear in the model (non-core ...factor model to link the non-core variables to the state variables of the DSGE ...
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DSGE model-based forecasting of non-modelled variables
... economy model developed by the Sveriges Riksbank (Adolfson, Laseen, Linde, and Villani, 2005 and 2008; Adolfson, Anders- son, Linde, Villani, and Vredin, 2007), the New Area-Wide Model developed at the ...
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KINEMATIC ENDPOINT VARIABLES AND PHYSICS BEYOND THE STANDARD MODEL
... CHAPTER 5 KINEMATIC ENDPOINT VARIABLES So far, we have discussed the Standard Model of particle physics and several its problems. We then discussed one extension to the SM that might solve these problems: ...
154
Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model
... • spatial constraints: take neighborhood information into account, resulting in object-based retrieval (e.g., [23]); • temporal constraints: use temporal autocorrelation to modify the cost function (e.g., [17,20]). Such ...
14
Designs for generalized linear models with several variables and model uncertainty
... quantitative variables, namely, vitamin concentration in the pre-packaging dip and the levels of two gases in the packing atmosphere, on several binary responses including the presence or absence of liquid in the ...
17
Correlated random variables in manpower planning – a shock model approach
... random variables. In manpower model discussed here is assumed that there is loss of manpower to a random extent at every decision epoch at which revised policies regarding wages, incentives and targets are ...
5
Forecasting macroeconomic variables using a structural state space model
... space model described in this paper nests the structural vector autore- gression (SVAR) ...Eleven variables are used, six of which are classified domestic as they can be considered to be within the sphere ...
20
Impact of underfill and other physical dimensions on Silicon Lateral IGBT package reliability using computer model with discrete and continuous design variables
... A passive thermo-mechanical finite element analysis using the element SOLID185 was undertaken in Ansys mechanical APDL. In passive thermal cycling, temperatures in the whole package change simultaneously during the ...
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