Regression models with ESN error distribution
Robustness for regression models with asymmetric error distribution
... For QQ plots we use log-rescaling for both axes. Moreover, on the graph, the true log- quantities on the x-axis are plotted against estimated log-quantities on the y-axis. In the literature QQ plots are shown in the ...
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Testing for symmetric error distribution in nonparametric regression models
... June 4, 2003 Abstract For the problem of testing symmetry of the error distribution in a nonparametric regression model we propose as a test statistic the difference between the two empir[r] ...
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Empirical likelihood estimators for the error distribution in nonparametric regression models
... the error distribution in nonparametric regression models can be improved when additional information about the distribution is included by the empirical likelihood ...
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Bootstrap tests for the error distribution in linear and nonparametric regression models
... the error distribution in lin- ear ...nonparametric regression model ...normal distribution has been recently considered by Akritas and van Keilegom ...
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A note on testing symmetry of the error distribution in linear regression models
... The results are depicted in Table 4.1 and show a reasonable approximation of the nominal level in all cases. The alternative of non-symmetry is detected with high probability, even in the case k = 4, which is rather ...
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Sequential regression measurement error models with application
... Sequential regression approaches can be used to analyze processes in which covariates are revealed in ...fitting regression models using the covariates revealed by the end of the current stage, but ...
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Goodness-of-fit tests for the error distribution in nonparametric regression
... heteroscedastic models are considered in the present ...multiple regression case is also investigated and a formal study of the bandwidth selection procedure is ...
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Estimation methods for regression models with unequal error variances
... These new techniques included the use of a prior likelihood function to induce "shrinkage" towards a common value among the estimators for the error varian[r] ...
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Regression Error Characteristic Optimisation of Non-Linear Models.
... of models A and B, taking only the portions which are in front, we can create an REC curve which illustrates the possible error/accuracy combinations given the available ...new models, C and ...
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Estimating the error distribution in semiparametric transformation models
... It may also be used for goodness-of-fit tests of a specified error distribution in a parametric or semiparametric regression setting, for testing the symmetry of the error distribution, [r] ...
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"A regression error specification test (RESET) for generalized linear models".
... for regression models assume that the response variable y follows the normal ...data models, qualitative response models, and duration data ...linear models (GLMs) deal with such ...
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Bootstrap Estimate of Prediction Error of Simple Linear Regression Models
... probability distribution for a random variable of ...prediction error using these two methods of bootstrap and discuss the performance of this ...prediction error of the simple linear ...
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Assessing prediction error of genetic variants in Cox regression models
... Cox regression models. That is because the Cox regression analysis requires complete data in all the variables included in the model, otherwise the data are ...
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Hausman tests for the error distribution in conditionally heteroskedastic models
... null distribution of χ 2 , and that it is consistent and able to detect the local alternative of order n −1/2 ...Generalized error distributions and Student’s t ...
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Computationally efficient goodness-of-fit tests for the error distribution in nonparametric regression
... the error distri- bution in nonparametric regression ...null distribution of the associated test statistics is usually approximated by means of a parametric bootstrap which, under certain conditions, ...
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Estimating the error distribution in nonparametric multiple regression with applications to model testing
... Gaussian process Z ( y ) with covariance function Cov ( Z ( y 1 ), Z ( y 2 )) = E [ { I (ε ≤ y 1 ) − F ε ( y 1 ) + ϕ(ε, y 1 )}{ I (ε ≤ y 2 ) − F ε ( y 2 ) + ϕ(ε, y 2 )} ] . Note that the above results are considerably ...
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Robust mixture regression models using t-distribution
... 1984), which is shown in Figure 1.1. In Cohen’s tone perception experiment, a pure funda- mental tone with electronically generated overtones added was played to a trained musician. The overtones were determined by a ...
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On the estimation of the joint distribution in regression models with censored responses
... multivariate distribution function F (x, y), Stute (21) proposed an extension of KM−estimator, and furnished asymptotic representation of integrals with respect to this estimator that turned out to have ...
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A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error
... heteroscedastic, and differential error. The details of the Bayesian MCMC method are presented in Web Appendix B. The MCMC algorithm was run for 100,000 iterations, with the last 40,000 iterations used for ...
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Estimation of Nonparametric Regression Models with Measurement Error Using Validation Data
... any error model structure between the surrogate and true covariables, we propose an estimator which integrates orthogonal series estimation and truncated series approximation ...
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