[PDF] Top 20 Higher Moment Estimators for Linear Regression Models With Errors in the Variables
Has 10000 "Higher Moment Estimators for Linear Regression Models With Errors in the Variables" found on our website. Below are the top 20 most common "Higher Moment Estimators for Linear Regression Models With Errors in the Variables".
Higher Moment Estimators for Linear Regression Models With Errors in the Variables
... used in empirical analyses contain errors of measurement. Such errors are probably relatively more important in macroeconomic studies [Morgenstern (1963), Langanskens and Van Rickeghem (1974), ... Voir le document complet
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Nonparametric regression with martingale increment errors
... algorithm for the construction of optimal adaptive ...introduced in [ 21 , 22 , 23 ], and it provides a way to select the bandwidth of a kernel estimator from the ...shares the ... Voir le document complet
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Bayesian Functional Linear Regression with Sparse Step Functions
... K, the number of intervals in the coefficient functions from the ...of the discretization of the rainfall, and the number of observations, the value of K should stay ... Voir le document complet
26
Asymptotic criteria for designs in nonlinear regression with model errors
... Introduction. In optimal experimental design one often assumes that there are no errors in the considered model, a condition rarely satisfied in ...model errors are usually ... Voir le document complet
10
Robust Sign-Based and Hodges-Lehmann Estimators in Linear Median Regressions with Heterogenous Serially Dependent Errors
... associated with different tested parameter values. In other words, if the null hypothesis has the form H 0 (β 0 ) : β = β 0 , the estimator corresponds to the value of β 0 which ... Voir le document complet
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Phase Transitions, Optimal Errors and Optimality of Message-Passing in Generalized Linear Models
... error in three classification problems as a function of the number of data-samples per dimension ...α. The red line is the Bayes-optimal generalization error, while the green one shows ... Voir le document complet
102
Forecasting temperature in a smart home with segmented linear regression
... reduce the number of hours of historical readings, but in fact we do not want to do ...squares regression and forward stepwise regression and found them to be ineffective [ 1 ...squared ... Voir le document complet
9
Adaptive Linear Models for Regression: improving prediction when population has changed
... tion models and the corresponding estimators is described ...below. In the sequel, the subscripts γ j will be associated with regression parameters of the ... Voir le document complet
40
Rank-based testing in linear models with stable errors
... (1968) for symmetric stable distributions with tail indices between 1 and 2, and Press (1972) for general α values, provide consistent ...and the method of indirect estimation described ... Voir le document complet
19
Sensitivity analysis with dependent random variables : Estimation of the Shapley effects for unknown input distribution and linear Gaussian models
... Conclusion In this chapter, we worked on the Gaussian linear framework approximation to estimate the Shapley effects, in order to take advantage of the simplicity brought by this ... Voir le document complet
299
Extension to mixed models of the Supervised Component-based Generalised Linear Regression
... the shrinkage parameter value increases with τ . On the other hand, the greater τ , the more Mixed-SCGLR (with l = 4 as recommended in [ 8 ]) focuses on the main ... Voir le document complet
13
Simulated Data for Linear Regression with Structured and Sparse Penalties
... 2.1 Linear Regression We place ourselves in the context of linear regression ...lies in a p-dimensional space; and let y ∈ R n denote the n- dimensional response ... Voir le document complet
12
Gaussian process regression with linear inequality constraints
... these models usually encompass symmetries, constraints on the sign of output variables, monotonicity with respect to some input variables or other constraints which are due to ... Voir le document complet
24
Asymptotic distribution of least square estimators for linear models with dependent errors
... The linear regression model with dependent errors has also been studied under more restrictive ...conditions. For instance, Pagan and Nicholls [ 14 ] consider the case ... Voir le document complet
19
Linear regression with stationary errors : the R package slm
... introduces the R package slm which stands for Stationary Linear ...Models. The package contains a set of statistical procedures for linear regression in ... Voir le document complet
32
Bayesian multivariate linear regression with application to change point models in hydrometeorological variables.
... Environment. The Broadback River is subject to two types of floods: spring flood, which are dominated by snowmelt, and summer – autumn floods which are caused by direct liquid ...presents the mean daily ... Voir le document complet
17
Experimental comparison of velocity estimators for a control moment gyroscope inverted pendulum
... estimation for a mechanical system is a well- known problem, see, for example, the recent work by Aranovskiy et ...and the references therein. In practice, a common solution is to ... Voir le document complet
7
Adaptive wavelet regression in random design and general errors with weakly dependent data
... is characterized by the following inequality: there exists three known constants, γ > 0, c > 0 and θ > 0 such that, for any m ∈ Z, a m ≤ γexp −c|m| θ . (2.1) This assumption is satisfied by a ... Voir le document complet
18
Estimation of Parametric Models with Conditional Heteroscedastic Errors
... Unit´e de recherche INRIA Lorraine, Technopˆole de Nancy-Brabois, Campus scientifique, ` NANCY 615 rue du Jardin Botanique, BP 101, 54600 VILLERS LES Unit´e de recherche INRIA Rennes, Ir[r] ... Voir le document complet
32
On combining wavelets expansion and sparse linear models for Regression on metabolomic data and biomarker selection
... sparse linear models for regression on metabolomic data and biomarker selection Nathalie Villa-Vialaneix 1,2∗ , Noslen Hern´andez 3 , Alain Paris 4 , C´eline Domange 5,6 , Nathalie Priymenko 7 ... Voir le document complet
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