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222,445
Nonlinear regressions with integrated time series
 Econometrica
, 2001
"... An asymptotic theory is developed for nonlinear regression with integrated processes. The models allow for nonlinear effects from unit root time series and therefore deal with the case of parametric nonlinear cointegration. The theory covers integrable and asymptotically homogeneous functions. Suffi ..."
Abstract

Cited by 96 (28 self)
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An asymptotic theory is developed for nonlinear regression with integrated processes. The models allow for nonlinear effects from unit root time series and therefore deal with the case of parametric nonlinear cointegration. The theory covers integrable and asymptotically homogeneous functions
On the Outlier Detection in Nonlinear Regression
"... Abstractâ€”The detection of outliers is very essential because of their responsibility for producing huge interpretative problem in linear as well as in nonlinear regression analysis. Much work has been accomplished on the identification of outlier in linear regression, but not in nonlinear regression ..."
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Abstractâ€”The detection of outliers is very essential because of their responsibility for producing huge interpretative problem in linear as well as in nonlinear regression analysis. Much work has been accomplished on the identification of outlier in linear regression, but not in nonlinear
Nonlinear Regression With Dependent Observations
 Econometrica
, 1984
"... This paper provides general conditions which ensure consistency and asymptotic normality for the nonlinear least squares estimator. These conditions apply to timeseries, crosssection, panel, or experimental data for single equations as well as systems of equations. The regression errors may be ser ..."
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Cited by 75 (4 self)
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This paper provides general conditions which ensure consistency and asymptotic normality for the nonlinear least squares estimator. These conditions apply to timeseries, crosssection, panel, or experimental data for single equations as well as systems of equations. The regression errors may
On fields of nonlinear regression models
"... Abstract. In the context of nonlinear regression, we consider the problem of explaining a variable y from a vector x of explanatory variables and from a vector t of conditionning variables, that influences the link function between y and x. A neural based solution is proposed in the form of a field ..."
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Abstract. In the context of nonlinear regression, we consider the problem of explaining a variable y from a vector x of explanatory variables and from a vector t of conditionning variables, that influences the link function between y and x. A neural based solution is proposed in the form of a field
Geometry Of The Nonlinear Regression With Prior
"... . In a nonlinear regression model with a given prior distribution, the estimator maximizing the posterior probability density is considered (a certain kind of Bayes estimator). It is shown that the prior influences essentially, but in a comprehensive way, the geometry of the model, including the int ..."
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. In a nonlinear regression model with a given prior distribution, the estimator maximizing the posterior probability density is considered (a certain kind of Bayes estimator). It is shown that the prior influences essentially, but in a comprehensive way, the geometry of the model, including
Tests of transformation in nonlinear regression
, 2004
"... This paper presents three versions of the Lagrange multiplier (LM) tests of transformation in nonlinear regression: (i) LM test based on expected information, (ii) LM test based on Hessian, and (iii) the LM test based on gradient. All three tests can be easily implemented through a nonlinear least s ..."
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This paper presents three versions of the Lagrange multiplier (LM) tests of transformation in nonlinear regression: (i) LM test based on expected information, (ii) LM test based on Hessian, and (iii) the LM test based on gradient. All three tests can be easily implemented through a nonlinear least
Inference in nonlinear regression
, 2008
"... This paper considers inference in a standard nonlinear regression model. We show that the model is nonregular in the sense that test statistics of interest exhibit a discontinuity in their limit distribution as a function of a parameter in the model. The discontinuity occurs when the coefficient ..."
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This paper considers inference in a standard nonlinear regression model. We show that the model is nonregular in the sense that test statistics of interest exhibit a discontinuity in their limit distribution as a function of a parameter in the model. The discontinuity occurs when the coefficient
A Comparison of Nonlinear Regression Codes
"... Five readily available software packages were tested on nonlinear regression test problems from the NIST Statistical Reference Datasets. None of the packages was consistently able to obtain solutions accurate to at least three digits. However, two of the packages were somewhat more reliable than the ..."
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Five readily available software packages were tested on nonlinear regression test problems from the NIST Statistical Reference Datasets. None of the packages was consistently able to obtain solutions accurate to at least three digits. However, two of the packages were somewhat more reliable than
Predictions In Nonlinear Regression Models
, 1997
"... Different predictors and their approximators in nonlinear prediction regression models are studied. The minimal value of the mean squared error (MSE) is derived. Some approximate formulae for the MSE of ordinary and weighted least squares predictors are given. ..."
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Different predictors and their approximators in nonlinear prediction regression models are studied. The minimal value of the mean squared error (MSE) is derived. Some approximate formulae for the MSE of ordinary and weighted least squares predictors are given.
Results 1  10
of
222,445