Testing goodness-of-fit for nonlinear regression models with heterogeneous variances

Caouder, Nathalie; Huet, Sylvie

doi:10.1016/s0167-9473(96)00049-7

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The huge example qualities of the test measurement are gotten from both the invalid and elective speculation. [5] portrays a goodness-of-fit technique for testing the parametric capacity for the regression model and the change in a parametric nonlinear regression model. [6] proposed a likelihood and restricted likelihood extent tests for decency of-attack of a nonlinear regression using first-request Taylor assessment gauge around the maximum likelihood estimator of the relapse boundary to harsh the invalid and elective theory is shown nonparametrically using penalized splines.…”

Section: Introductionmentioning

confidence: 99%

Goodness of Fit Test of an Autocorrelated Time Series Cubic Smoothing Spline Model

Adams

Obaromi²,

Irinews

2021

J. Nig. Soc. Phys. Sci.

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We investigated the finite properties as well as the goodness of fit test for the cubic smoothing spline selection methods like the Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV) and Mallow CP criterion (MCP) estimators for time-series observation when there is the presence of Autocorrelation in the error term of the model. The Monte-Carlo study considered 1,000 replication with six sample sizes: 30; 60; 120; 240; 480 and 960, four degree of autocorrelations; 0.1; 0.3; 0.5; and 0.9 and three smoothing parameters; lambdaGML= 0.07271685, lambdaGCV= 0.005146929, lambdaMCP= 0.7095105. The cubic smoothing spline selection methods were also applied to a real-life dataset. The Predictive mean square error, R-square, and adjusted R-square criteria for assessing finite properties and goodness of fit among competing models discovered that the performance of the estimators is affected by changes in the sample sizes and autocorrelation levels of the simulated and real-life data set. The study concluded that the Generalized Cross-Validation estimator provides a better fit for Autocorrelated time series observation. It is recommended that the GCV works well at the four autocorrelation levels and provides the best fit for time-series observations at all sample sizes considered. This study can be applied to; non –parametric regression, non –parametric forecasting, spatial, survival and econometric observations.

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Section: Introductionmentioning

confidence: 99%